EMPLOYEES
Master Thesis, Master of Science, Human Resource Management University of Groningen, Faculty of Business and Management
2 ABSTRACT
The COVID-19 pandemic forced the majority of Dutch employees to work from
home. This is a unique opportunity to measure the influence of telework on work-life balance
(WLB) of employees, since WLB is a widely discussed subject in the literature conducting
telework with contradicting results. Therefore, this study analyses the difference in WLB in
the most extreme case: being forced to work from home fulltime. Data of an ongoing field
study with more than 600 respondents at a Dutch Bank is used for analysing the change in
WLB during the pandemic, covering a time period of 5 months (January to May 2020).
Furthermore, the role of having children and having a work routine is analysed and discussed.
The difference between WLB of employees before and during telework is analysed by a
paired sample t-test and a linear regression analysis. Results showed that WLB of employees
did not significantly change after the implementation of telework. Higher age, having a work
routine and WLB before the pandemic turned out to be positive predictors of WLB during the
pandemic. Besides, having young children (age 0-11) negatively influenced WLB during the
pandemic.
3 INTRODUCTION
The 2019 coronavirus disease (COVID-19) is the largest pandemic since the SARS
pandemic in 2003 (Liu, Gayle, Wilder-Smith & Rocklöv, 2020). The pandemic started in the
city of Wuhan, in China, and has spread in a couple of weeks to more than 100 other countries
paired with 6.4 million confirmed cases (Remuzzi & Remuzzi, 2020; Almukhtar et al., 2020).
Governments all over the world took measures to prevent further spread. In the Netherlands,
the prime minister asked employees to work from home as much as possible in March 2020
(Klaassen, 2020). For many employees, this meant a radical change in their work
environment. Of all Dutch businesses, 54% changed from working at the office to
teleworking, also known as telecommuting, at home (Wolters, 2020). As a result, the
pandemic created new work-related challenges (Rudolph et al., 2020).
Teleworking is not a new phenomenon. Nilles (1994) defined teleworking as
‘’working outside the conventional workplace and communicating with it by way of telecommunications or computer-based technology’’. The use of telework is growing and
many researchers studied the consequences of teleworking for firms and their employees,
such as increased job satisfaction and performance and a change in work-life balance
(Gajendran & Harrison, 2007). Improvement of work-life balance is for employees often one
of the reasons to choose for telework (Doherty, Andrey & Johnson, 2000). Clark (2002)
defined balance as ‘’satisfaction and good functioning at work and home, with a minimum of role conflict’’. Tausig and Fenwick (2001) describe the ‘problem’ of work-life balance as
follows: individuals experience demands of work and demands of family or personal life and
it can be challenging to control the balance between them. Several studies found that
teleworking indeed helps by balancing work and home obligations (Dockery & Bawa, 2018).
However, other researchers found negative effects of teleworking on work-life balance.
4 Some researchers state that telework could increase conflicts between work and family
(Doherty, Andrey & Johnson, 2000), while other studies consider telework as a great
opportunity to create balance between work and family demands (Fletcher & Bailyn, 1996).
Overall, the existing literature about the effects of teleworking on work-life balance, including
work-family balance, is often contradicting (Gajendran & Harrison, 2007; Raghuram &
Wiesenfeld, 2004). But there are differences between ‘normal’ teleworking and teleworking
during the COVID-19 pandemic. First and foremost, its usually voluntary nature compared to
the forced nature of teleworking during the pandemic.
During the COVID-19 pandemic, employees were forced to work from home
full-time. Normally, one or several ‘teleworking’ days were just a part of the complete working
month of employees and it can be assumed that the consequences of teleworking depend on
the intensity of teleworking (Bailey & Kurland, 2002). Furthermore, employees could often volunteer to do telework at home if they considered it to be beneficial (Bailey & Kurland,
2002). Past research focussed on this partial, voluntary telework (Chong, Huang & Chang,
2020). There is a significant difference between choosing to work from home and the
mandatory form of telework during the COVID-19 pandemic (Chong, Huang & Chang,
2020). During the pandemic, organisations had to implement telework unprepared and
employees were forced to adjust to telework instantly (Carillo, Cachat-Rosset, Marsan, Saba
& Klarsfeld, 2020).
This paper contributes to the existing literature by studying the effects of telework
during an emergency situation in which employees were forced to work fulltime from home.
This paper focuses on the change of work-life balance during the pandemic since various
demands, such as work and family demands, increased in this period, while a little is known
about balancing these demands during a crisis (Rudolph et al., 2020, Eby, Mitchell &
5 were not the only changes in work and private life of employees. In the Netherlands, the
government also decided to close the day care centres for children, elementary schools, high
schools and universities when the pandemic hit the county in March (Rijksoverheid, 2020).
This means children had to stay at home, unless their parents had a job with vital importance,
for example when they were working at a hospital (Rijksoverheid, 2020). Furthermore, if they
went to elementary school, high school or university, children and young adults had to do
their school assignments from home. Home-schooling is not only a radical change for the
children but also for their teleworking parents. Doing telework while children are at home can
influence employees’ work-life balance, since there is no clear separation between work and
family life when conducted at the same location (Ahrentzen, 1990). According to Heck, Owen
& Rowe (1995) the need to arrange care for dependent family members, for example children,
increases the conflict between work and family domains.
Normally, having a routine at work helps to set temporal boundaries between work
and private life. One can assume that the strict temporal boundary between work and private
life blurred when employees work from home during the COVID-19 pandemic, since it is
more likely that the employee faced conflicting demands in his or her time (Shamir &
Salomon, 1985). Defining schedules helps employees who telework by setting a boundary
between work and home (Ahrentzen, 1990). The work routine of employees can change to
accomplish a better balance between work and home.
This paper focuses on the change in work-life balance of employees who are forced to
work from home during the COVID-19 pandemic. This paper aims to examine the following
research question: ‘’To what extend causes telework from home a change in the work-life
balance of employees during the COVID-19 pandemic?’’. In addition to studying work-life balance, this research will also focus on the role of having children and a work routine. By
6 home, this paper fills a gap in the literature concerning the influence of telework on work-life
balance of employees. Telework during the pandemic has some similarities with normal
telework, but it is considered as a unique context with specific characteristics (Carillo et al.,
2020). While existing literature concerning the COVID-19 pandemic often tends to focus on
general well-being measures (Möhring et al., 2020), this study compares data of the same
respondents before and during the pandemic. Moreover, according to Rudolph et al. (2020),
there is a lack of empirical evidence about the role of work routines and about managing work
from home with childcare. In addition, this paper is also relevant for firms that currently deal
with teleworking. Although fulltime teleworking has benefits, there are often too many
obstacles to make it work. Consequently, the COVID-19 pandemic can be seen as the biggest
experiment in the history of teleworking which could help determining the characteristics
influencing the success of telework (Kramer & Kramer, 2020). This crisis is an unique
opportunity to lay bare the challenges of teleworking and make improvement for the future
(Rudolph et al., 2020). This paper can thus be used to be prepared for telework during
possible pandemics or other crises in the future. Besides that, it is likely that the COVID-19 pandemic changed the way we work permanently and telework will be more common in the future (Carillo et al., 2020).
THEORETICAL FRAMEWORK
According to Kreiner, Hollesbe & Sheep (2009), changes in the work environment,
such as teleworking, can affect the work-life balance of employees. The relationship between
teleworking and work-life balance is frequently discussed in the existing literature with mixed
results. Employees consider teleworking often as an opportunity to improve their work-life
balance (Doherty, Andrey & Johnson, 2000). Several studies show indeed a positive
7 schedule their work and non-work activities themselves (Van der Lippe & Lippényi, 2018).
Other studies show a negative relationship and overall, we can state that the results are
inconclusive (Van der Lippe & Lippényi, 2018; Raghuram & Wiesenfeld, 2004). Telework
during the COVID-19 pandemic is different from ‘normal’ telework as described in the
existing literature. Normally, employees only telework for a couple of days per month (Bailey
& Kurland, 2002). In the current situation, employees are forced to work at home fulltime.
Furthermore, they have to manage other circumstances, such as increased work and non-work
demands (Rudolph et al., 2020). When these demands are incompatible, it can cause a role
conflict (Shamir & Salomon, 1985). Bronfenbenner (1979) defined a role as ‘’a set of
activities and relations expected of a person occupying a particular position in society, and of
others, in relation to that person.’’ Individuals have different roles at work and at home. Bringing these roles together in the same space can cause a role conflict (Ahrentzen 1990).
According to the boundary theory, employees cross boundaries when they exit and
enter a role (Ashfort, Kreiner & Fugate, 2000). Normally, physical boundaries separate work
and non-work demands (Staines, 1980). But due to telework during COVID-19, the physical
boundaries have vanished. Therefore, it is expected that it will be harder for employees to
balance between work and increased non-work demands.
Hypothesis 1: During COVID-19 pandemic, work-life balance will decrease (=WLB at T1/T2 (=T1) < WLB at T3/T4 (=T2).
Work-family conflict is frequently used as an example of role conflict (Behson, 2002;
Duxbury, Higgins & Neufeld, 1998). Telework is often described as a possible solution to
manage work and family demands, by giving employees more discretion in juggling their ‘at
home’ and ‘at work’ (Dockery & Bawa, 2018; Van der Lippe & Lippényi, 2018). But there
are also studies showing that telework is not helping to improve work-family balance. In an
8 working from home arrangements since they could not manage the expectations from their
family members when they worked at home. Similarly, Kossek, Colquitt & Noe (2001)
describe that employees with children face a greater number of demands and expectations
when they work from home, because it is easier for family members to approach them.
Telework and having children
In addition to the implementation of telework, the (partial) lockdown incused other
challenges for employees with children, since schools and day care centres were temporally
closed. According to Oreopoulos, Page and Stevens (2003), the amount of time available for
home schooling is one of the most important conditions to make home schooling work. In a
‘normal’ situation, it is already hard for employees to manage their time between work and family (Behson, 2002). When employees telework during COVID-19, even more time is
required to help their children with school, which makes it likely that time-based conflicts
increase (Rudolph et al., 2020). Not only the schools, but also daycare facilities closed during
the pandemic. Younger children are highly dependent on care of parents and their needs could
increase the conflict between work and family demands (Heck, Owen & Rowe, 1995). In the
Netherlands, schools and daycare facilities were closed until 11 May 2020. For this study, I
use data of April and May and therefore the challenges of home schooling are not applicable
for the whole period. However, I expect that balancing between work and non-work demands
during telework is harder for employees with children in general, even if these children are
going to school.
9 Telework and routines
A possible solution for controlling work and non-work demands, is setting boundaries. Individuals can create boundaries to help them ordering their environment (Ashforth, Kreiner
& Fugate, 2000). Park, Fritz & Jex (2011) describe the importance of boundaries to prevent
overlap between home and work domains. Kreiner, Hollesbe & Sheep (2009) classified four
boundary tactics: behavioral, temporal, physical and communicative boundaries. A possible
boundary to avoid role conflicts for homeworkers is a temporal boundary. According to
Shamir & Solomon (1985), temporal boundaries can be seen as a necessary condition for
managing conflicting demands which occur when employees work from home. Work
schedules or routines are often mentioned as a temporal boundary between work and
non-work. According to Arhentzen (1990), defining a schedule helps homeworkers to avoid role
conflicts, by separating work and home activities in time. Besides the increased demands in
work and non-work domains, working from home promotes longer overall working hours
(Dockery & Bawa, 2014), while time for recovering after workdays is important for
maintaining the wellbeing of employees (Sonnentag, 2003). Furthermore, having a daily
routine is important, since working from home with changing schedules and a lack of
structure can cause stress (Tausig & Fenwick, 2001). Overall, by teleworking during
COVID-19, it is expected that work routines are important for controlling work and non-work
demands and recovery.
Hypothesis 3. The decrease in WLB during COVID-19 pandemic from T1 to T2 will be smaller for employees with work routines at T2.
Telework and interaction between routines and having children
The boundaries between work and life domains are most important for employees with
10 completely disappear (Duxbury, Higgins & Neufeld, 1998). As a result, it can be difficult for
employees to mentally separate work and family demands when working from home (Hill,
Hawkins & Miller, 1996). Setting temporal boundaries can help distinguish work and family
time. Furthermore, employees could create routines that match the schedules of their family
members to reduce work family conflict (Pitt-Catsouphes & Morchetta, 1991). Therefore, I
expect that the negative influence of having children on WLB during the pandemic can be
moderated by creating work routines.
Hypothesis 4. The negative influence of having children on WLB T2 will be buffered by having work routines at T2. The steepest decrease in WLB is expected for employees a) with children and b) without work routines. The smallest decrease in WLB is expected for
employees a) without children and b) with work routines.
METHOD Procedure
Data for this study is originating from an ongoing data collection at a Dutch bank, that
analysed the effects of unlimited leave on general well-being, work-related well-being and
organisational outcomes. In the original study, a field experiment was used with two groups
11 unlimited leave (control group). Team leaders could decide if they wanted their team to take
part in the experiment. The teams were then divided into experimental and control group in
January 2020. In this study, I did not separate the respondents based on unlimited leave, but
this variable was added as a control variable in my analyses. Instead, data of all respondents
was used to analyse differences before and during the COVID-19 pandemic. This was
possible since one of the outcomes of the original study was the work-life balance of the
employees. The study started in January 2020, before the Corona outbreak, and lasted until
December 2020. My data concerned the period between January and May 2020. Participants
received an online questionnaire every month about their experiences of the past month. To
encourage employees to fill in the questionnaires, a lottery was organised where the
employees could win a weekend break (5 prices in total). The more questionnaires they filled
in, the more lottery tickets they received. Respondents had to fill in at least 5 of the 12
questionnaires for taking part in the lottery. Furthermore, every employee that filled in at least
10 questionnaires received a gift voucher of 25 euros. The questionnaire was conducted
anonymously in order to encourage honest answering. I compared work-life balance of the
two months before (T1) and two months after the start of the lockdown (T2) in the
Netherlands. The Corona outbreak and the start of teleworking took place in March and most
of the respondents were forced to work from home since the 16th of March. As a result, it was
hard to define if the answers to the questionnaire of March were related to teleworking or
working at the office. Therefore, I compared the data of January and February (T1) with the
data of April and May (T2). In both periods (T1 and T2), there was no change in unlimited
12 Participants
The data gathered for this research concerns more than 600 questionnaires submitted
by respondents working at a Dutch bank. The employees were working at different locations
in the Netherlands, before teleworking started. The sample included employees of various
departments. For this research, data of 349 employees is used, since data is excluded when the
data of WLB T1 or WLB T2 was missing (see statistical analyses). Besides that, an
examination of outliers has been done. I used the answers of the questions about the age of
respondents and the age of their youngest child to make a boxplot in order to see if there were
deviant results. Deviant results could mean that respondents did not fill in the questionnaire
completely honestly which could harm the study and affect the reliability of the findings. Both
boxplots did not show any outliers and therefore no respondents were excluded. In this
sample, 73.3% of the respondents were male and 26.7% of the respondents were female. The
average age of the respondents was 43.6 years (SD = 10.5). A majority of the respondents
(57.8%) had at least one child.
Measures
The questionnaire of the original study about the effect of unlimited leave included
more than 30 variables. I focussed on the variables work-life balance, family status and work
routine.
Work-life balance. The measurement of work-life balance was based on the scale presented by Syrek, Brauer-Emmel, Antoni & Klusemann (2011). Work-life balance was
initially assessed with 5 statements: ‘’I was satisfied with the balance between my work and private life’’, ‘’It was easy for me to balance my work and private life’’, ‘’I was meeting the requirements of both my work and my private life’’, ‘’I managed to achieve a good balance
13 my priorities are set in relation to my job and personal life’’. All items were measured with a
5-points scale ranging from strongly disagree (1) to strongly agree (5). During the first months
of the original study, it turned out that not all respondents filled in the complete questionnaire
as it was very time-consuming. Therefore, a factor-analysis was done to decide which
questions did not have a lot of influence for the results and therefore could be deleted and the
questionnaire shortened. As a result, the last item about work-life balance was deleted, from
March onwards. In this study, I also deleted the data of last item for the results of January and
February to be consistent across the study. I used a reliability analysis to decide if the
different items can be combined into one variable of work-life balance. This applied for the
results of every month (January, February, April and May). The Cronbach’s alpha of January
was .903, of February was .914, of April was .877, and of May was .907.
Having children. The questionnaire started with descriptive questions, including one question about the family status of the respondents: ‘’Do you live by yourself or with
someone else?’’. There were 5 answer options for this question: ‘’I live with myself’’, ‘’I live with my spouse or partner’’, ‘’I live with my spouse/partner and a child/children’’, ‘’I live with a child’’ and ‘’Other’’. The answers to this question were transformed into a new variable, in which living with children (yes or no) constituted the only separation.
Work routine. The question about work routine was added to the questionnaire of March and April, because teleworking started in March. I used the data of the question of April, since
data of March is not taken into consideration in this study. The question was as follows:
‘’During the last four weeks, how much has the structure of your day (e.g., waking hours, working times, lunch or coffee breaks) changed?’’. There were three answer options: ‘’I more
14 a work routine. I calculated a new variable in order to separate between having a routine (old
or new) and having no routine. Besides these options there was an open field where
respondents could add comments it they wanted to explain why or how their routines have
changed. I used these comments for explaining the results of the quantitative data.
Statistical analyses
In order to test the hypotheses, the program IBM SPSS Statistics was used. The results
of work life balance of January and February were combined as T1 and the results of April
and May were combined as T2. First, the four items of WLB each month were combined into
a new variable, for example WLB of January. This was possible because the Cronbach’s
alpha of all items in every month was above .8. If respondents answered some of the items of
WLB but not all of them, the available answers were used as the average WLB. Then, the
average WLB of January as well as February were combined into a new variable: WLB at T1.
The same applies for the items of April and May (T2). If respondents answered none of the
questions about work-life balance in one questionnaire, one of the two months in T1 or T2
was used as average their average. If the data was missing of both months of T1 or T2, the
data of the participants was excluded from the analyses.
Analysis hypothesis 1. In order to test if the WLB of employees decreased during the pandemic, a paired sample t-test was used to test the difference of WLB T1 and WLB T2.
This test was used since I wanted to compare the data of two periods within the same sample.
Missing values of WLB T1 or WLB T2 were excluded and therefore the sample size of this
test consisted of 349 respondents.
Analyses for hypotheses 2, 3 and 4. H2, H3 and H4 were tested by conducting a
multiple regression analysis. This regression tested if WLB T1, having children and having a
15 children multiplied with work routine. The first step was testing if WLB at T1 was a predictor
of WLB at T2. During this first step, the control variables age, gender and group type (control
or experimental) were also added to verify that these variables did not influence WLB during
the pandemic. After testing H2, having children and having a work routine were entered as a
possible second and third predictor. Eventually, a multiplication of work routine and having
children was done to analyse their interaction effect. In order to avoid multicollinearity, the
standardized values of having children and having a work routine were used for the
multiplication. This interaction term was tested as the last possible predictor of WLB at T2.
After each time a new predictor was entered, the change of R² was analysed to measure if the
proportion of variance that is predicted increased.
RESULTS
Preliminary analyses. Means, standard deviations and correlations for all study
variables were calculated (Table 1). The Pearson correlation analysis (Table 1) reveals several
significant correlations between variables and WLB T2. Firstly, WLB T2 was positively
correlated with age (r = .17, p < .01), WLB T2 (r = .35, p < .01) and work routine (r = .28, p <
16 TABLE 1
Means, standard deviations, and correlation between study variables
As mentioned before, items that measured WLB were combined into a new variable,
since the Cronbach’s alpha allowed. An graph presenting the development of WLB in
January, Februrary, April and May was made to get an overview of the change of WLB
before and during the pandemic. was made in order to get an overview of the change of WLB
before and during the pandemic (see graph 1). Data of March is not taken into account in this
graph, since telework started in the middle of this month. Average WLB in January was 3.42,
17 GRAPH 1
WLB of employees before and during the pandemic
In the questionnaire, participants were asked about their work routine during the
pandemic. The participants had three answer options and the results are represented in Figure
1. This pie chart shows that 35.59% of the participants sticked to old routines, 47.12%
developed new routines and 17.29% did not have any routines. Based on literature and
hypotheses, a new variable was created which separated having a routine and having no
routine. In other words, having the same routines as before and having new routines are taken
18 FIGURE 1
Work routine during the pandemic
Development of WLB
Work-life balance in the months before and during the pandemic is displayed in Graph
1. The mean WLB before the pandemic (T1) was 3.47 (SD = .80) and the mean WLB during
the pandemic (T2) was 3.37 (SD = .80). Even though there seemed to be a decrease in WLB
before and during the pandemic, this decrease was not significant, t(348) = 1.85, p = 0.07.
Therefore, hypothesis 1 was rejected. In Graph 1, it seemed there was a decrease of WLB in
April, the first month of telework. Therefore, I did a paired sample t-test again, using only
April instead of April and May. Results showed a significant difference in WLB between T1
(M = 3.48, SD = .77) and April (M = 3.31, SD = .83), t(294) = 3.06, p = .00. This means there
was a significant decrease in WLB in April compared to the period before the pandemic.
There was a small difference of WLB T1 between the two tests, since the first test used data
19 Children and work routines
For testing H2, H3 and H4, a multiple regression was conducted. In this analysis,
different variables were entered to analyse if and to what extent they predicted WLB during
the pandemic (T2). Each step leading to the results of the regression analysis will be
explained. The results of the three steps are also displayed in Table 2.
In the first step of the multiple regression analysis, the control variables WLB T1, age,
gender and group type (i.e. experimental vs. control group) were added to measure if and to
what extent they influenced WLB T2. Results showed a positive effect of WLB T1 on WLB
T2. This means that employees who experienced a better WLB before the pandemic also
experienced a better WLB during the pandemic. Furthermore, results showed that age was
also a positive predictor of WLB T2, meaning that older employees experienced a better WLB
during the pandemic. This was a surprising result, as I did not consider age to be an influential
factor for WLB. Age and WLB T1 predicted 18.2% of the variance of WLB T2. Results
further showed that gender was not a significant predictor of WLB T2. The final control
variable, the group to which the respondent belonged, was also not a significant predictor of
WLB T2, meaning that work-life balance during the pandemic was not dependent on having
limited or unlimited leave.
In the second step of the regression analysis, the variables ‘’children’’ and ‘’work routine’’ were added. Results showed that having children was not a significant predictor for WLB during the pandemic and therefore hypothesis 2 was rejected. Having a work routine
had a positive effect on WLB T2. This means that developing a new work routine or sticking
at an old work routine positively influenced WLB during the pandemic compared to having
no routine. Having a work routine predicted 5.4% of the variance of WLB T2. Hence,
20 The last step of the multiple regression analysis was used to measure if having a work
routine moderated the negative effect of having children on WLB T2. Model 2 revealed that
having children had no significant influence at a 95% confidence interval level, however it
would have significant influence at a 90% confidence interval level. Therefore, there was a
need to analyse whether the interaction variable influenced this effect. Results showed that the
interaction of having children and having a work routine had no significant influence. In other
words, the influence of having children on WLB during the pandemic was not buffered by
having a work routine at T2. Therefore, hypothesis 4 was rejected.
21 In conclusion, no strong evidence was found for a change of WLB during the pandemic.
Results showed that WLB before the pandemic and age positively influenced WLB during the
pandemic whereas gender and group type (experimental or control) did not influence WLB of
employees during the pandemic. Furthermore, having a work routine had a positive influence
on WLB during the pandemic compared to having no work routine. No strong evidence was
found for the (negative) influence of having children on WLB during the pandemic. Lastly,
results did not show that having a work routine could buffer the possible negative effect of
having children on WLB during the pandemic. WLB T1, age and work routine were all
significant predictors of WLB T2 and predicted 23.6% of the variance of WLB T2.
Post-hoc analysis
Work routines. Originally, respondents had three options to describe their work routine (see figure 1). As followed from the literature, it was unclear whether having a new or an old
work routine would lead to different outcomes of WLB during the pandemic. Therefore, I did
not differentiate between having an old and a new work routine. Yet, results showed that
having a (new or old) routine compared to having no routine had significant influence on
WLB T2. In the light of this result, it is also interesting to analyse if there were differences
between sticking to one’s old or developing a new work routines. To shed light on this
question of the effects of new and old work routines, I have tested the difference in WLB T2
between people with new, old and no work routines. For this analysis, a one-way ANOVA
test is used with WLB T2 as dependent variable and the original variable of work routine,
with three answer options, as independent variable. Results of the one-way ANOVA showed
that there was a significant difference between the three groups (F(2,292) = 13.072, p < .001).
Consequently, a Tukey post-hoc analysis was used to analyse between which groups there
22 routine (M = 2.92, SD = .78) was significantly lower (p < .001) compared to respondents with a new (M = 3.43, SD = 0.74) and old (M = 3.53, SD = .67) work routine. There was no
significant difference between a new and an old work routine (p = .559), meaning that having a work routine was beneficial for WLB T2, irrespective of whether this was a new or an old routine. So, having routines seems to relate to higher levels of WLB.
Age youngest child. Results showed that having children did not negatively influence WLB T2. This result was surprising, since I expected that having children would negatively
influence WLB when doing telework. A possible explanation for this outcome is the fact that
in the regression analysis, no separation was made between younger and older children.
However, it is likely that there is a difference in demands depending on the age of children.
During the pandemic, young children were especially dependent on their parents, since
parents had to assist their children with school work (age 4-11). Moreover, the youngest
children (age 0-4) require almost constant attention (Hogarth, Terence, Hasluck & Gaelle,
2000). In the questionnaire, only the age of the youngest child was asked. Hence, I made a
new variable, in which I separated (1) employees without children, (2) employees with a child
or children older than 11 and (3) employees with at least one child younger between 0 and 11
years old. I expected that older children (age > 11) were less dependent on their parents and
were already used to homework, since they were going to secondary school. Again, a linear
regression analysis was done with the new variable (‘’having a young child’’) instead of
having children in general. Besides that, a new interaction variable was calculated with the
standardised scores of having a work routine and having a young child.
Step 2 of the regression showed a negative effect, (B = .12, t(236) = -2.36, p = .02) of
having a young child on WLB T2. In step 2 of the analysis, WLB T1, age, work routine and
23 variance of WLB T2. In order to analyse if the difference in WLB applied for all three groups
(employees without children, employees with a child or children > 11 years and employees
with at least one child ≤ 11 years), an one-way ANOVA test was used with WLB T2 as dependent variable and having a young child as independent variable. Results showed a
significant difference between groups (F(2.280) = 4.467, p = .01). A post-hoc Tukey test was
used to analyse between which groups there was a significant difference. Results showed a
significant difference in WLB T2 between employees with a child ≤ 11 years (M = 3.18, SD = .83) and employees without children (M = 3.48, SD = .73). No significant difference was
found between employees with a child or children > 11 years (M = 3.48, SD = .86) and
employees with a child ≤ 11 years (p = .06). There was also no difference in WLB T2 between employees without children and employees with a child or children > 11 years (p =
1). These results showed that having a child or children younger than 12 years negatively
influenced WLB during the pandemic.
Step 3 of the regression showed a significant influence of the interaction term, (B =
.09, t(235) = 2.12, p = .04), meaning that having a work routine buffered the negative
influence of having at least one young child. Step 3 showed that WLB T1, age, work routine,
having a young child (≤ 11 years) and the interaction between having a young child and a work routine were all significant predictors of WLB T2 and predicted 24.3% of the variance
of WLB T2 together.
Open comments on routine. Respondents had three answer options to represent their work routine. Besides that, there was also an open field in which respondents could describe
experienced changes during the pandemic. These comments were useful to explain the results
of the quantitative data of work routine. In the questionnaire of March, respondents were
24 questionnaire of April, respondents were asked to describe how the pandemic affected their
work life, work routines or leave patterns. However, the questionnaire of March was not taken
into account in this study, this question was explicit addressed to the period of telework and
therefore the open comments of both March and April were used. I will shortly discuss the
main outcomes of the comments in order to get more insight of the underlying reasons of
(not) having a work routine.
Overall, respondents tried to stick to their old routines to structure their day. However,
there were circumstances that made it hard to stick to this routine. Firstly, many respondents
mentioned that they scheduled their work activities around the schedule of their children
and/or partner. For example, one respondent described: ‘’In the morning, I am a teacher for
my children. In the afternoon, I am a bank employee’’. Other employees worked from 6 a.m. till 9 a.m. and finished their work in the evening. Several employees described it was hard to
match their working schedule with the schedule of their child(ren), since working schedules
were not always flexible. Sometimes, meetings were planned at moments when employees
actually wanted to educate their children.
Secondly, respondents mentioned that they experienced no clear boundary between
work and non-work time and that it was hard for them to dissociate from work, especially in
the evening. Some employees felt the urge to work more hours than they would normally do.
For example, one respondent described: ‘’I feel more pressure to finish my tasks, while I
would not have finished them when I worked at the office’’. Besides, short ‘’coffee’’ breaks disappeared. The lunch break was often the only break during the day, and some employees
even ate their lunch at their desk. Furthermore, some employees used their normal travel time
for work, while other employees used this time for non-work activities.
In short, employees tried to schedule their day in a similar manner as before the
25 schedule, especially when living with children. Some employees succeeded in developing a
fitting work routine, but often it took several weeks to create this new routine.
DISCUSSION
The purpose of this study was to find out if and to what extent telework during the
COVID-19 pandemic caused a change in work-life balance of employees. Moreover, I
focused on the influence of having children and having a work routine on WLB of employees
during the pandemic. Key findings of this study and explanations of these findings will be
discussed.
Work-life balance during the pandemic
Change in WLB. When I compared T1 to T2, I did not find a significant change in WLB during the pandemic. Hence, H1 was rejected. The fact that there was no significant
decrease or increase in WLB is actually in line with existing literature, since the findings
about effects of telework on work-life balance of employees are often contradicting
(Gajendran & Harrison, 2007; Raghuram & Wiesenfeld, 2004). However, I did expect a
decrease in WLB due to certain circumstances of the pandemic, such as increased non-work
demands (Rudolph et al., 2020). Existing literature provides some possible explanations for
the results. Several researchers observed that the ability of employees to manage work and
non-work demand depends on the supportiveness of the organisation (Van der Lippe &
Lippényi, 2018; Haddad, Lyons & Chatterjee, 2009). Especially during a crisis, feeling
supported could be important for employees. In the ongoing field study, which data I used for
this study, respondents were asked to what degree they felt supported in March and April on a
5-point likert scale. Results showed that in both months, employees felt supported by the
26 of the organisation in the crisis situation prevented a decrease in WLB. When I compared T1
to the first month (April) instead of the first two months of telework, I did find a significant
decrease in WLB. This means that WLB of employees decreased in the first period of
telework. Possibly, it was harder for employees to manage work and non-work demands in
the first weeks of telework since the implementation of telework was abrupt and employees
were unprepared. It is assumable that in May, employees were more used to the new
circumstances and maintained a better balance between work and non-work activities.
Influence WLB T1. Results showed that WLB T1 positively influenced WLB T2. This means that employees with a better balance in work and non-work demands before the
pandemic also experienced a better balance during the pandemic. Guest (2002) describes in
his review article about WLB that several individual characteristics are determinants of
achieving WLB. Examples of these characteristics are work orientation, personality and
personal control. Possibly, individuals who were more capable of balancing work and
non-work demands before the pandemic, are also better in balancing these demands when doing
telework.
Influence age. This study showed that age positively influenced WLB T2, meaning that older employees found a better balance in work and non-work demands during the
pandemic. In this study, age was used as a control variable. This is often the case in literature
concerning WLB and these studies showed mixed results (Schieman, Glavin & Milkie, 2009).
According to Soomro, Breitenecker & Shah (2018) and Richert-Kaźmierska & Stankiewicz (2016), older employees are more satisfied with their WLB in general. Older employees already achieved several milestones in their work and private life, such as raising children and
getting promoted, while younger employees are still trying to target these milestones
(Soomro, Breitenecker & Shah, 2018). Another possible explanation for this result is that
27 members when they work from home (Baert, Lippens, Moens, Weytjens & Sterkens, 2020).
As a result, older employees experienced less conflicts between work and family demands
(Baert et al., 2020).
Telework and having children
I hypothesized that having children had a negative effect on WLB T2, since family
demands increased during the pandemic (Rudolph et al., 2020). Initially, I did not find
evidence for the effect of having one or more children on work-life balance during the
pandemic and therefore hypothesis 2 was rejected. However, when I separated the employees
with children into employees with at least one child younger than 12 and employees with
children older than 11, I did find a significant effect. Having at least one child younger than
12 negatively influenced WLB of employees during the pandemic. This result is in line with
research of Moore (2006). According to this study, telework has only a negative effect on
employees’ well-being when there are young children at home. The younger the children, the higher their influence on employees’ well-being (Moore, 2006). It is assumable that during the pandemic, the influence of young children was even higher than in a normal situation. I
expect that especially the demands of children between the ages of 4 and 11 were higher than
normal, since they needed assistance with their school work. Unfortunately, only the age of
the youngest child was documented in my dataset, meaning I could not specifically study the
influence of children between the ages of 4 and 11.
Telework and work routine
An important outcome of this study is the demonstrated importance of having a work
routine when employees work from home. Results showed, as expected, that employees with
28 employees without a work routine. Hence, hypothesis 3 was supported. No difference in WLB
has been found between employees who developed new routines (47.12%) or employees who
stuck to old routines (35.59%). Tausig & Fenwick (2001) provide a possible explanation for
this result. According to their study, WLB depends on the perception of employees’ schedule
control. Some employees with schedule control change their schedule while other employees
stick to their old schedule (Tausig & Fenwick, 2001). It is possible that employees with old as
well as employees with new routines perceived the same level of schedule control and
therefore their WLB did not differ. A minority of employees did not have any routines during
telework (17.29%). Since not having routines negatively influenced WLB, it is interesting to
shed light on the underlying reasons for not having a routine. Firstly, comments showed that
some employees simply experienced it as impossible to create a work routine that fits their work and non-work demands. Another finding was that some employees found it hard to dissociate from work. Examples are employees who worked more hours than normal,
especially in the evening, and employees that left out their breaks. It seemed like work was
more important for them than non-work activities. England & Misumi (1986) named this concept work centrality: ‘’The degree of general importance that working has in the life of an individual at any given point of time’’. Employees with a high degree of work centrality possibly did not feel the urge to stick to a work routine in order to create a boundary between work and non-work activities.
Telework and interaction between work routines and children
I expected that having a work routine would especially be important for employees
with children, since a work routine could provide a border between work and family roles of
employees. Results indicated that there was no interaction between work routines and children
in general, but post-hoc analysis showed that having a work routine does buffer the negative
29 routine was especially important for employees with young children to maintain WLB.
Several employees wrote in the comments that they planned their work activities around the
activities of family members. For example, some employees called themselves teachers
during the day and employees in the evening. Others mentioned that their work schedule
changed from day to day, depending on the demands of their children. Normally, the
flexibility to match work schedules with schedules of children is one of the benefits of
telework (Hilbrecht, Shaw, Johnson & Andrey, 2008). However, during the pandemic,
employees experienced overlap between demands of work and demands of children at the
same time. According to Schneider & Harknett (2019), uncertain work routines increase
work-life conflict. Most employees with young children (80.3%) succeeded in developing
new or sticking to old work routines during telework. WLB of those employees was less
negatively affected by having young children.
Theoretical implications
First of all, this study contributes to the literature of telework and work-life balance,
by shedding a new light on the relationship between telework and WLB during a crisis
situation. Normally, telework in the literature is voluntary and partial (Chong, Huang &
Chang, 2020), while in this study, telework is fulltime and mandatory due to the COVID-19
pandemic. This new form of telework can enrich the existing literature by defining challenges
of- and possible solutions for successful telework (Kramer & Kramer, 2020; Rudolph et al.,
2020). Results of this paper particularly contribute to existing literature by explaining the
importance of having a work routine for balancing work and non-work demands.
Furthermore, this study showed that having a work routine is especially important for
employees with young children (< 11 years). According to Rudolph et al. (2020), empirical
evidence about the role of work routines and managing work and childcare was lacking in the
30 Secondly, this study contributes to the boundary theory and literature about role
overlap by studying a situation in which telework is mandatory and schools and daycare
centres were closed. Especially employees with young children experienced role overlap in
situations in which they had to choose between their working role and parenting role. This
study provides evidence of the importance of setting temporal boundaries when doing
telework. Temporal boundaries can differentiate between time for work and time for children
and this helps employees to prevent role overlap.
Practical implications
During the pandemic many organisations implemented telework unprepared while
they were unprepared, but it is possible that the COVID-19 pandemic will change the way we
work permanently (Carillo et al., 2020). Results of this study can be used to help employees
balance work and non-work demands. Of all respondents, 82.71% was able to stick to old or
develop new routines. Hopefully, this study makes the remaining employees more aware of
the importance of having a work routine. Employees with a working partner at home could
define a work routine together so they make sure that they spend time together, for example
during lunch (Brown, Tierney & Hunt, 2013). This creates a boundary between work and
non-work demands. Furthermore, employers and managers could support employees to create new
routines or stick to old routines. According to Walia (2015) a coaching leadership style is
important for improving WLB of employees. Besides, employers and managers should
identify which employees need support in developing work routines. Employees perceive
different levels of boundary control, meaning that for some employees it is easier to set
temporal boundaries than for others (Kossek, Ruderman, Braddy & Hannum, 2012).
Furthermore, employers and managers could prevent over-working by clearly discussing
31 important that these recommendations are considered by employers and managers when
telework becomes the new norm, or when organisations face a next crisis such as a pandemic.
Limitations and future research
This study has strengths, for example the accessibility to data before and during the
pandemic and the sample size, but this study also has some limitations. Firstly, not all months
of telework during the pandemic are taken into consideration in this study. Employees filled
in the questionnaire at the end of each month and telework started in the middle of March.
Therefore, I could not determine if the data of March belonged to the period before or during
the pandemic. It is unfortunate that the data of the first two weeks of the pandemic was not
taken into account. Today we know at the moment of writing this paper, that the period of
telework during the pandemic took longer than only May and April. I started analysing the
results in the summer of 2020. In this period, (partial) lockdowns in most European countries
ended and some organisations decided to work at the office again. Therefore, I chose to
analyse two periods of two months before and during the pandemic. The other months of
2020 were not taken into account in this study, while most employees did still work from
home due to the pandemic. This can be considered as a limitation of the study. Possibly,
employees are more used to telework nowadays and have already succeeded in developing
fitting work routines. Future research could focus on the change of WLB over a longer period
of telework during the COVID-19 pandemic.
Secondly, the results of this study are not generalisable for all employees, for example
because respondents of this study all worked for the same employer (a Dutch bank).
According to Baruch (2002) the success of telework depends on the job, the organisation and
the individual. Job characteristics could influence the difficulty of managing work and
non-work demands during telenon-work (Dingel & Neiman, 2020). Employees who use computers, in
32 example employees in the IT sector (Iscan & Naktiyok, 2005). Telework will be more
difficult for employees who are working in sectors such as transport and health care (Alipour,
Falk & Schüller, 2020). For these employees, changing to telework could lead to increased
work demands. Jobs of employees in my sample were quite suitable for telework and
therefore future research could focus on employees who will face more difficulties when
doing telework in a crisis situation. Besides, the influence of telework on WLB of employees
could depend on several characteristics of the organisation. As mentioned before,
supportiveness of the organisation can influence the ability of managing work and non-work
demands (Van der Lippe & Lippényi, 2018; Baruch, 2002). Furthermore, according to Baruch
(2002), a culture of trust is needed for effective telework. Future research could focus on the
influence of organisation characteristics on WLB during telework by using data from different
organisations. Lastly, individual characteristics (i.e. attitude, values, norms, qualities and
needs) influence the success of telework (Baruch, 2002). According to Hobbs & Armstrong
(1998), qualities such as self-motivation, discipline and adaptability are important for
effective telework. Individual characteristics were not taken into considerations in this study.
Hence, future research could focus on the importance of individual characteristics for
balancing between work and non-work demands during telework in a crisis situation, such as
a pandemic.
CONCLUSION
33 to an old routine (before the pandemic) both positively influenced WLB during the pandemic. Moreover, age turned out to be a positive predictor of WLB during the pandemic.
Furthermore, findings suggest only having young children (age 0 - 11) influenced WLB during the pandemic. To conclude, this study contributes to the existing literature of telework by analysing WLB of employees in a situation in which telework is mandatory and
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