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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 1
Understanding Developers Well-Being and
Productivity: A Longitudinal Analysis of the
COVID-19 Pandemic
Daniel Russo, Member, IEEE, Paul H. P. Hanel and Niels van Berkel
Abstract—COVID-19 has likely been the most disruptive event
at a global scale the world experienced since WWII. Our disci-
pline never experienced such a phenomenon, whereby software
engineers were forced to abruptly work from home. Nearly
every developer started new working habits and organizational
routines, while trying to stay mentally healthy and productive
during the lockdowns. We are now starting to realize that some
of these new habits and routines may stick with us in the future.
Therefore, it is of importance to understand how we have worked
from home so far. We investigated whether 15 psychological,
social, and situational variables such as quality of social contacts
or loneliness predict software engineers’ well-being and produc-
tivity across a four wave longitudinal study of over 14 months.
Additionally, we tested whether there were changes in any of
these variables across time. We found that developers’ well-being
and quality of social contacts improved between April 2020 and
July 2021, while their emotional loneliness went down. Other
variables, such as productivity and boredom have not changed.
We further found that developers’ stress measured in May 2020
negatively predicted their well-being 14 months later, even after
controlling for many other variables. Finally, comparisons of
women and men, as well as between developers residing in the UK
and USA, were not statistically different but revealed substantial
similarities.
Index Terms—COVID-19, Human Factors, Productivity, Well-
Being, Longitudinal Analysis.
I. INTRODUCTION
T
HE COVID-19 pandemic and the subsequent lockdowns
have likely been the among the most disruptive events that
most software engineers faced during their lifetime. Suddenly,
professionals started to work from home, potentially alongside
family members. This peculiar situation is unprecedented
in computer science history; thus, we have virtually no
information about the impact of lockdowns on the well-being
and productivity of software professionals.
The only related evidence comes from the effects of
quarantined people in previous epidemic outbreaks, which
suggests that isolation and lockdown measures are a huge
burden to individuals’ well-being [
1
] and productivity [
2
].
Indeed, well-being and productivity are two crucial aspects
of our lives, particularly during extraordinary events: Well-
being is a fundamental human right, according to the Universal
Declaration of Human Rights whereas productivity provides us
with the earnings to ideally maintain or improve our lifestyle.
D. Russo and N. van Berkel are with the Department of Computer Science,
Aalborg University, Denmark. Email: daniel.russo@cs.aau.dk
Paul H. P. Hanel is with the Department of Psychology, University of Essex.
Manuscript received November 19, 2021; revised ....
Health professionals already identified some relevant predictors
of well-being during harmful events [
1
], [
3
]. However, this
research is often cross-sectional (i.e., not longitudinal), only
includes a limited number of predictors, focusses on well-
being while ignoring productivity. The software engineering
community also reacted quickly to this event by performing a
large study which found that home office ergonomics, disaster
preparedness, and fear are correlated with well-being and
productivity [
4
]. Nevertheless, this was also conducted cross-
sectionally and with only a few predictors. Pre-pandemic
research on remote work [
5
] might provide some indications.
However, it is unlikely that such research is still relevant during
a global pandemic, with professionals locked down in their
houses without childcare or usual welfare support provided
during non-pandemic times.
For these reasons, we believe it is essential to investigate
the well-being and productivity of software professionals
continuously and longitudinally across the entire COVID-19
pandemic (as of Summer 2021). By doing so, we aimed
to achieve several goals. First, identify relevant predictors
of both well-being and productivity of software engineers
working from home in a stressful context such as a lockdown.
Second, test for causal relations between the identified variables
and if well-being predicts productivity or vice versa (i.e.,
scholars found that they are interrelated but could not find a
causal association [
6
], [
7
], [
8
]). Third, test whether well-being,
productivity, and other relevant variables such as loneliness,
social contacts, and need fulfillment changed of the course
of 14 months since the beginning of the first lockdown in
spring 2020. Fourth, provide data-driven recommendations
about possible future lockdowns. Fifth, understand how to
improve developers’ work-life balance while working from
home in a post-pandemic setting and contribute to the nascent
literature about the future of work. Hence, we formulate our
research questions as follows:
Research Question 1
:How have well-being, productivity, and
other relevant social and psychological variables changed
throughout the COVID-19 pandemic?
Research Question 2
:Which variables predict Well-being and
Productivity over time?
To answer our research questions, we surveyed 192 globally
distributed software engineers four times over a period of
14 months. We assessed their well-being and productivity,
alongside 15 other variables. To guide our research design,
arXiv:2111.10349v1 [cs.SE] 19 Nov 2021
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2
we grounded our investigation in organizational [
9
] and
psychological [
10
] theories, which are relevant for people’s
well-being and productivity. For example, self-determination
theory [
10
] assumes that human motivation can be divided
into three basic needs which are also linked with work
motivation [
11
]: the needs for autonomy, competence, and
relatedness. Additionally, we also included evidence from the
remote work literature [
12
], [
13
], [
14
], and recommendations
by health and work authorities [15], [16], [17].
We analyzed our data using a range of different statistical
approaches tailored to the specific questions. Specifically, to test
whether well-being, productivity, and 15 variables including
loneliness, needs, and social contacts have changed, we used
17 within-subject ANOVAs. To test whether well-being and
productivity would be predicted over time by any of the 15
carefully selected variables (see section
III-B
), we used six
cross-lagged panel models. To assess whether there are any
mean differences between women and men and participants
living in the UK and USA, we used a series of between-subject
t-tests. Results suggest that developers’ well-being and their
quality of social contacts increased throughout the pandemic
(i.e., between April 2020 and July 2021), while their emotional
loneliness decreased. Productivity remained unchanged. Further,
only stress at time 2 predicted developers’ well-being at time
4. Finally, we found no mean differences between women and
men or people living in the UK and USA for any of the 17
variables we measured across all four waves.
This article has the following structure. Section II discusses
the related work of well-being and productivity in the related
work literature, as also recent advancements in the software
engineering community. The Research Design and Analysis
is then described in Section III. Following, in Section IV, we
discuss the results of our analyses, as the implications and
recommendations for professionals and software houses in
Section V. Finally, we conclude our work by outlying future
research directions in Section VI.
II. RE LATE D WOR K
Following the abrupt onset of the COVID-19 pandemic
and subsequent lockdowns, COVID-19 related research has
expanded rapidly. Health scientists started investigating coun-
termeasures to reduce the spread and impact of the virus and
studied the psychological and physiological effects on people
living in lockdown conditions. Also, in the software engineering
community, the effect of the pandemic on software developers
has gained increased attention. After describing the state of the
art of the research on Well-Being and Productivity in Remote
Work, we focus on the software engineering contributions.
A. Well-Being and Productivity in Remote Work
There is a consensus that lockdown measures have a negative
impact on well-being [
1
], [
18
]. In particular, research shows
that living in a lockdown can result in increased experiences
of anger, depression, emotional exhaustion, fear of infecting
others or getting infected, insomnia, irritability, loneliness, low
mood, post-traumatic stress disorders, and stress [
19
], [
20
],
[
21
], [
22
], [
23
], [
24
]. Additionally, fears of e.g., infection [
25
],
[
26
], lack of supplies or not being treated [
27
], and misleading
or contradictory information [
28
] can result in significantly
increased stress levels. Moreover, the psychological effects of
being locked down may appear years after [1].
On the other hand, pre-COVID research shows that remote
working is associated with an improved work-life balance, cre-
ativity, productivity, reduced stress, and low carbon emissions
due to the absence of commuting [
29
], [
13
], [
14
], [
30
], [
31
],
[
32
]. Nevertheless, there are also some apparent drawbacks
related to remote work, such as deteriorating collaboration and
communication, loneliness, feeling of being constantly ‘online,’
decreasing motivation, and distractions at home [
33
]. Besides
such aspects, forecasts suggest that remote work will increase
on a large scale in the next years [29], [34].
For this reason, research opportunities are extensive, also
in the years to come. There are plenty of open questions,
such as which variables and the extent to which these variables
influence well-being and productivity in combination. Studying,
e.g., the stress in remote work, without considering all the
variables involved, provides little overall guidance for software
engineering teams because it is unclear whether stress is more
strongly related to well-being than, for example, loneliness or
anxiety. Therefore, the presented paper studies these variables
together rather than separately to identify the variable(s) most
strongly associated with well-being and productivity.
B. Software Engineering and COVID-19
Overall, the software engineering community has been quite
active in researching pandemic-related aspects. We identified
relevant work through Scopus and arXiv (considering that this
research topic is highly contemporary, some papers are still
under review).
The first works in this research area are from the late
90s with broader use of the internet. Pounder (1998) [
35
]
was the first relevant contribution we identified, with an
essay about security problems linked to telework. In the early
2000s, Guo (2001) [
36
] performed two qualitative surveys on
software process improvement related to the distinctive nature
of teleworking. Similarly, Higa et al. (2000) [
37
] studied how
e-mail usage influences telework.
Afterward, there has been a twenty-year gap, with only
two exceptions. James & Griffiths (2014) [
38
] developed a
mobile execution environment to support a secure and portable
working from home setting. Ford et al. (2019) [
39
] interviewed
three transgender software engineers to explore the interplay
of gender identity and remote work.
Following the start of the pandemic and the first lockdown,
two research groups performed survey studies. Ralph et al.
(2020) [
4
] performed a cross-sectional study of over two thou-
sand globally distributed developers working from home during
the pandemic where an a priori research model derived by
literature was validated through Structural Equation Modeling.
Russo et al. (2020) [
8
] went in the opposite direction. Rather
than having a top-down model to validate, they employed an
exploratory approach looking at the most relevant variables
related to either well-being or productivity and analyzed the
data through a longitudinal design.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 3
Microsoft has also been active in understanding the effects
of the pandemic on its employees. Ford et al. (2020) [
40
]
surveyed Microsoft’s developers twice. They found that the
quality of family life and time improved, although remote
work introduced a lack of focus, poor work-life boundaries,
and communication and sync issues. Similarly, Miller et al.
(2021) [
41
] performed two surveys in which they collected
information about working from home and team-related issues.
They found that communication and interaction with colleagues
are relevant predictors of developers’ satisfaction and team
productivity. Butler & Jaffe (2021) [
42
] conducted a 10-week
diary study. Identified challenges from remote work were
meetings, overwork, and physical and mental health. However,
Microsoft developers appreciated more family time and work
flexibility.
More recent studies focus on particular aspects of remote
work. For example, Cucola
s
,
& Russo (2021) [
43
], with a Mixed-
Methods research design, investigated how Scrum software
development adapted to working from home. According to their
results, the home-working environment is the most crucial vari-
able for a software project’s success. Also, self-determination
theory [
10
] (i.e., the need for autonomy, competence, and
relatedness) is a valuable theoretical lens to improve working
from home conditions, as they are linked with well-being [
44
],
for example. Finally, Machado et al. (2021) [
45
] surveyed
233 Brazilian software professionals and investigated gender
differences. They concluded that the pandemic affected women
more negatively than men. In contrast, Russo et al. did not
found any meaningful gender differences [8].
From a content perspective, half of the papers are concerned
with specific topics related to remote work i.e., security [
35
],
[
38
], process [
36
], work productivity [
37
], and inclusion [
39
].
Where the other half focused on well-being and productivity
aspects of remote work [
40
], [
4
], [
8
], [
42
], [
45
], [
46
] and
productivity related to project characteristics [47], [43].
III. RESEARCH DESIGN
To design our research, we followed the ACM SIGSOFT Em-
pirical Standards for Longitudinal Studies [
48
]. Consequently,
we asked carefully recruited software professionals to complete
the same survey four times, over a period of 14 months. Wave 1
was collected between 26-30 April 2020, wave 2 between 10-13
May 2020, wave 3 between 24 February and 3 March 2021, and
wave 4 between 29 June and 5 July 2021. Wave 1 and 2 were
only two weeks apart since we were initially only interested in
the stability of predictors of well-being and productivity. Wave
3 was collected in late winter 2021 when the number of COVID-
19 cases in most Western countries decreased again, and wave
4 was when a significant part of people in Western countries
had received an offer to get vaccinated. Unique randomized
IDs were assigned to participants to preserve their anonymity
and track their participation across all four waves.
A. Participants
The sample size was initially determined to be able to
detect a small-to-medium effect size of
f
= .15 for a repeated-
measurement (within-subject) ANOVA, using a power of .80
TABLE I
OVE RVIE W OF SA MP LE’S E DU CATIO NAL ATTA INM EN T AND L OC ATION O F
WAVE 4 .
N% of sample
Less than high school degree 0 0%
High school graduate 4 3.2%
Some college but no degree 16 12.9%
Bachelor’s degree 63 50.8%
Master’s degree 35 28.2%
Doctoral degree 6 4.8%
United Kingdom 39 31.5%
United States 30 24.2%
Portugal 14 11.3%
Italy 6 4.8%
Ireland 6 4.8%
Other 29 23.4%
and a corrected
α−
level of .004 (see section
III-C
1 for a
justification of the lower
α−
level ). A power analysis using
G*Power [
49
] revealed that we would need a sample size
of at least 102 participants who participated in all four data
collection waves. We selected participants from a pool of
over 500 software engineers as previously identified [
50
].
These informants have been selected through a multi-screen
process, where we assessed for representativeness through pre-
screening (both in terms of computer programming experience
and profession, but also task quality on the data collection
platform), competence screening (competency-based questions
on software design and programming), and quality screening
(attention checks). Through additional screening questions, we
subsequently narrowed this pool down to 192 professionals. In
particular, we looked for informants who were working from
home during the pandemic for at least 50% of their time and did
not live in countries with jeopardized COVID regulations (e.g.,
Germany). 192 software engineers completed the first survey
(
Mage
= 36.65 years,
SD
= 10.77, range = 19–63; 154 men,
38 women), 184 participated in wave 2, 144 in wave 3, 124 in
wave 4, and 107 participated in all four waves and completed
all measures. Similarly, to ensure data consistency, we only
included participants living in countries with comparable
lockdown policies (e.g., excluding countires like Germany
who enforced different policies among the Laender or Sweden
with a rather liberal approach to the pandemic). Demographic
information are provided in Table I. We ensured high data
quality by recruiting participants from the data collection
platform Prolific Academic [
51
] and compensated participants
above the USA’s minimum wage. Additionally, none of our
participants failed any attention checks or completed the survey
in a concise time, which further ensures the quality of our data.
The survey was run using the platform Qualtrics.
To collect the data, we attained ourselves to the ethical
guidelines of the Declaration of Helsinki [
52
]. All participants
were at least 18 years old and expressed their consent to
participate in the study each time. Also, they were free to
withdraw at any point. The lead author also completed formal
training in research ethics for engineering and behavioral
sciences.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 4
B. Measurements
Well-being and productivity are two complementary variables
of a healthy working environment. Not surprisingly, they are
correlated [
8
]. Especially in exceptional times, such as a
pandemic, organizations should prioritize employees’ mental
and physical well-being if they want to be productive. On the
other hand, as suggested by Russo et al. [
8
], contributing to
the organization’s value is important for the sense of belonging
or achieving of every developer. Therefore, productivity does
also contribute to professionals’ well-being [8].
Consequently, productivity and well-being are our two
outcome variables (i.e., dependent variables). To identify
relevant predictors (or our independent variables) of our
dependent variables, we started from the insights of Russo
et al. [
8
]. Namely, we included in this analysis only the 15
(out of 50) predictors which correlated with at least one of the
outcome variables (i.e.,
r≥|.30|
) [
8
]. This was done to keep
the number of predictor variables to a manageable amount.
All variables were measured using self-reported measures,
which is very common in the literature [
4
], [
50
]. The internal
consistency of the scales was quantified with Cronbach’s
α
and ranged from satisfactory to very good. Values above .60
and .70 are desirable for exploratory and confirmatory research,
respectively [53].
To measure the identified variables we only used either
validated scales or adapted items from scales used in previous
publications with high reliabilities. The only exceptions were
‘productivity’, ‘quality and quantity of communication with
colleagues and line managers’, and ‘daily routines’ for which
we created our own items because we could not find existing
scales suitable for our purposes. Responses were mostly given
on 5-, 6-, or 7-point response scales with higher values
indicating a higher score on each variable. Every scale is
briefly subsequently described with its name, reference, and
reliability metrics (i.e., Cronbach’s alpha) across all the four
data collection waves. In this paper, we use the terms ‘wave’
and ‘time’ interchangeably. For a detailed descriptions of the
items see Russo et al. [8].
Well-being
. We measured well-being with the 5-item Satis-
faction with Life Scale [
54
]. Participants were asked to report
their well-being using items such as ”I was satisfied with my life
in the past week” on a 7-point Likert scale (1: Strongly disagree,
7: Strongly agree). The Cronbach’s
α
values to measure internal
consistency for all four data collection waves were the following
αtime1=.90
,
αtime2=.90
,
αtime3=.92
,
αtime4=.94
.
Productivity
. There is no agreement among researchers on
how productivity can be measured. For example, measuring
productivity in an allegedly objective way by using function
points [
55
] has been criticized as detrimental in the long
run [
56
]. Further, the objective approach is barely feasible
if participants work in different areas since comparisons
across work are very challenging. Therefore, other researchers
advocated using self-reports [
57
], which has apparent short-
comings such as subjectivity. In the present research, we
developed a subjective approach to reduce social desirability by
making the survey anonymous. Specifically, we operationalized
productivity as a function of time spent working and efficiency
per hour, compared to a typical, pre-pandemic week. The
reason for this choice is that we wanted to investigate
productivity while working remotely as compared to being
in the office. Since our measure does not allow to compute
internal consistency, we instead computed test-retest reliability
by correlating the productivity scores at time 1 with those at
time t2 (rit =.50, p < .001).
Boredom
was measured with the Boredom Proneness
Scale [
3
], [
58
];
α1=.87
,
α2=.87
,
αtime3=.92
,
αtime4=.90.
Self-blame and behavioral disengagement
, two coping
strategies, were measured with the respective subdimensions
of the Brief COPE scale [
59
]. Cronbach’s
α
’s for self-blame
were
α1=.75
,
α2=.71
,
αtime3=.92
,
αtime4=.92
,
and for behavioral disengagement
α1=.76
,
α2=.71
,
αtime3=.89,αtime4=.91.
Distractions at home
was measured with a 2-item scale
we developed (
α1=.64
,
α2=.63
,
αtime3=.75
,
αtime4=.65.
Generalized anxiety
was measured with an adapted ver-
sion of the
7
-item Generalized Anxiety Disorder scale [
60
];
α1=.93,α2=.93,αtime3=.94,αtime4=.95.
Emotional and social loneliness
were measured with the
De Jong Gierveld Loneliness Scale [
61
]. Emotional loneliness’
Cronbach’s
α
-levels were:
α1=.68
,
α2=.69
,
αtime3=.68
,
αtime4=.73
, and for social loneliness:
α1=.84,α2=.87,αtime3=.90,αtime4=.88.
Autonomy, competence, and relatedness
were measured
with the psychological needs scale [
62
]. Need for autonomy’s
Cronbach’s
α
-levels were:
α1=.72
,
α2=.76
,
αtime3=.77
,
αtime4=.78
; for Competence:
α1=.77
,
α2=.65
,
αtime3=.77
,
αtime4=.79
; and for Relatedness:
α1=.79,α2=.78,αtime3=.78,αtime4=.80.
Quality of social contacts
were measured with 3-items,
two of which were adapted from the social relationship quality
scale [
63
] and one was developed by us,
α1=.73
,
α2=.77
,
αtime3=.76,αtime4=.84.
Quality and quantity of communication with colleagues
and line managers
were measured with a self-developed
3-item scale (
α1=.88
,
α2=.92
,
αtime3=.93
,
αtime4=.94).
Stress
was measured with the Perceived Stress Scale [
64
];
α1=.80,α2=.77,αtime3=.83,αtime4=.78.
Daily Routines
were measured by a self-developed 5-item
scale (
α1=.75
,
α2=.78
,
αtime3=.81
,
αtime4=.78
.
Extraversion
was measured with a subscale of the Brief
HEXACO Inventory [
65
];
α1=.71
,
α2=.69
,
αtime3=.75,αtime4=.61.
C. Analysis
In total, we used three different types of analyses, which
seemed most appropriate to us, to answer our research question
and to perform additional exploratory analysis. Below, we
briefly describe and justify each of them.
Raw data, R-code to reproduce our analyses, and the zero-
order correlations for all 17 variables, separately per wave
and across all data collection waves, are included in the
supplemental materials.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 5
1) Changes along the COVID-19 Pandemic: To test whether
any change between the four data collection waves occurred, we
ran a series of 17 repeated-measures ANOVAs, one per variable.
This allowed us to test if, for example, software engineer’s well-
being increased, decreased, or remained the same. Additional
to the common descriptive (means and standard deviations)
and inferential statistics (F-value
1
and
p
-value), we report as
an effect size how many participants report a higher, lower,
or equal level of any variable at time 4 compared to time 1.
Given the number of 17 tests of variables, which are, however,
mostly correlated with each other, we set our
α
-level to .004.
That is, we only consider findings to be significant if
p<.004
.
This threshold is, in our view, neither conservative nor liberal.
However, we acknowledge that other researchers might prefer
a more conservative or liberal threshold. We, therefore, report
the exact
p
-values, which allows researchers to select different
thresholds.
2) Exploring causality: To test whether any of the 15
predictor variables predict our two outcome variables at time
4, well-being and productivity, we ran six cross-lagged panel
models in which we regressed well-being or productivity at
time 4 onto all predictors, and, crucially, both outcomes. It
is essential to also include, for example, well-being at time
1 as a predictor for well-being at time 4, because otherwise,
we might erroneously conclude that, for instance, anxiety is
related to only the aspects of anxiety that are correlated with
well-being. We realize that there are different views about
inferring causality between two variables, A and B. While
some have a stringent view on causality, which requires being
able to rule out that any third variable is responsible for the
association between A and B (for an overview see [
66
]), others
argue that it is sufficient to show that A is correlated with B
and A is measured before B [67]. A middle point is to argue
that A measured at time 1 needs to predict B at time 2 while
controlling for B measured at time 1, to be able to state that
A causally predicts B [
68
]. We use this view and go a step
further by also controlling for a range of other variables. This
approach has two advantages over a series of models with
only one predictor (e.g., stress) and one outcome (e.g., well-
being), which are also common in the literature. First, using
only one variable as predictor as opposed to 15 would have
resulted in many more models and we would had therefore
needed to control for many comparisons. Second, by controlling
for many related variables, our approach is conservative as it
focuses on the unique impact of each predictor variable. For
example, by simultaneously including anxiety, stress, loneliness
alongside other variables as predictors of well-being into the
same model, we focus on the unique impact of each predictor
on well-being. Further, we only focused on well-being and
productivity at time 4 because it is the most recent wave, and
it is crucial to allow the outcomes to vary (most measured
variables are stable over time [
8
]). If, for example, well-being
at time 1 would be very highly correlated with well-being
at a subsequent time, there would be minimal variance for
1
The F-value is a test-statistic that increases with larger mean-differences,
lower within-group variability or larger sample size. It is, for fixed sample
size, inversely related to the p-value which is used to determine whether our
findings are statistically significant.
the other predictors to explain because well-being at time 1
would already explain most of the variance. Thus, we ran two
cross-lagged panel models (one with well-being and one with
productivity as predictor) with variables measured at time 1,
two with variables measured at time 2, and two with variables
measured at time 3 as independent variables. Given the total
amount of six comparisons, we set our
α
-threshold to .008.
Note that we are adjusting the
α
-threshold based on the number
of comparisons per type of analysis. For example, we ran six
cross-lagged panel models, but 17 repeated measures ANOVAs.
Thus, the α-threshold had to be different.
3) Between-group comparisons: Additionally, we compared
women and men, and people living in the United Kingdom and
the USA (these were the two countries from which relatively
most of our participants came from) across all 17 variables
and all 4 time points, resulting in
2×4×17 = 136
between-
subject t-tests. We, therefore, adjusted our
alpha−
threshold to
.0005. To address recent calls to report effect sizes that display
similarities to avoid a one-sided focus on potentially small
differences [
69
], we also report the effect size Percentages
of Common Responses (PCR) alongside the more common
effect size Cohen’s
d
. PCR is a measure of overlap between
two groups (e.g., women and men) and ranges from 0 (no
overlap/similarities) to 100 (both groups overlap perfectly).
IV. RES ULT S
In the first step, we tested for construct validity by correlating
all 17 variables with each other separately for each data
collection wave. The zero-order Pearson correlations across all
waves were as expected. For example, well-being correlated
negatively with stress, loneliness, and boredom, and positively
with need for autonomy, competence, and relatedness, which
is in line with the literature [
70
], [
71
], [
8
]. Details of those
tests are in the Supplementary Materials.
A. Changes along COVID-19 Pandemic
The results of the 17 repeated-measures ANOVA are dis-
played in Table II. Four of the ANOVAs were significant.
Well-being (Fig 1), quality of social contacts (Fig 5), and self-
blame (Fig 3) increased and emotional loneliness decreased
(Fig 4). Behavioral disengagement was higher at time 2 than
at time 1 but went down to the starting point at times 3 and
4. For well-being, for example, 77 developers reported higher
levels at time 4 than at time 1, 38 lower levels, and 9 an equal
amount of well-being (cf. Tab. II). In contrast, productivity
remained stable over time (Fig 2).2
B. Exploring causality
We ran six cross-lagged panel models to test which variable
causally explains well-being and which productivity at time 4.
In the first model, we used well-being as outcome and all
17 variables listed in Table II measured at time 1 as predictors.
2
Note that the repeated-measures ANOVAs necessarily only included the
107 participants that took part in all four waves whereas descriptive statistics
reported in Table II and Figures 1 to 5 all participants in each wave (e.g.,
192 in wave 1) to improve prevision of the statistics we report.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 6
Variable M1 SD1 M2 SD2 M3 SD3 M4 SD4 F-value p-value Greater Smaller Equal
Well-being 4.14 1.37 4.34 1.29 4.4 1.45 4.7 1.45 10.25 < .001 77 38 9
Productivity 0.99 0.42 1.03 0.44 1.07 0.44 1.13 0.51 3.36 0.1329 74 43 4
Boredom 2.94 1.14 2.93 1.16 2.83 1.27 2.77 1.18 0.98 0.4036 52 64 8
Behavioral disengagement 1.8 0.94 2.06 1.03 1.88 1.11 1.84 1.07 5.49 0.001 36 28 60
Distraction at home 2.47 0.93 2.44 0.9 2.41 0.96 2.38 0.92 0.33 0.8013 39 47 38
Self-blame 1.81 0.99 1.88 1.01 2.28 1.29 2.25 1.26 18.68 < .001 56 21 47
Generalized anxiety 2.25 1 2.17 1.01 2.2 1.07 2.38 0.92 1.1 0.3492 62 51 11
Emotional loneliness 2.11 0.9 2.01 0.87 2.1 0.91 1.88 0.9 6.49 < .001 36 66 22
Social loneliness 2.64 1 2.56 1.02 2.79 1.08 2.73 1.04 4.41 0.0045 52 51 21
Need for relatedness 3.5 0.83 3.56 0.8 3.48 0.84 3.59 0.82 1.91 0.1265 65 46 13
Need for competence 3.57 0.74 3.58 0.73 3.62 0.76 3.67 0.74 0.59 0.6187 55 51 18
Need for autonomy 3.48 0.69 3.51 0.73 3.42 0.77 3.51 0.77 2.49 0.0599 57 49 18
Quality of social contacts 4.11 1.09 4.31 1.08 4.07 1.12 4.26 1.13 4.5 0.004 66 43 15
Communication 4.53 1 4.29 1.19 4.44 1.21 4.38 1.2 2.68 0.0465 43 49 27
Stress 2.5 0.81 2.52 0.8 2.52 0.88 2.44 0.85 1.43 0.232 44 56 24
Daily routines 4.68 1.56 4.72 1.53 4.83 1.58 4.82 1.58 0.26 0.8552 48 52 24
Extraversion 3.45 0.79 3.46 0.78 3.47 0.8 3.46 0.71 0.37 0.772 49 44 31
TABLE II
WITHIN-SUBJECT ANOVAS F OR A LL 17 VARIABLES,SIG NIFI CA NT VARI ABL ES AT p
≤
0.004 HIGHLIGHTED.
Mn
RE PRE SE NTS T HE ME AN VAL UE OF E ACH
WAVE AN D SDnI TS STA NDA RD DE VI ATION .
Fig. 1. Well-being across time. The red line displays the trend over time,
whereas the box at each time point shows the range in which the middle 50%
of the data falls. Responses were given on a 7-point scale ranging from 1 to 7.
The overall model was significant,
R2=.41, adj.R2=
.32, F (17,101) = 4.21, p < .001
. Non-surprisingly, well-being
at time 1 predicted well-being at time 4,
B= 0.42, SE =
.11, p < .001
, indicating high stability of developers’ well-
being across time. However, none of the other variables was
significant.
In the second model, we used productivity at time 4 as
outcome and all 17 variables measured at time 1 as predictors.
The overall model was not significant,
R=.10, adj.R2=
−.04, F (17,118) = 0.73, p =.77
. In the third model, we
used well-being as outcome and all 17 variables measured
at time 2 as predictors. The overall model was significant,
R2= 52, adj.R2=.44, F (17,97) = 6.28, p < .001
. Non-
surprisingly, well-being at time 2 significantly predicted well-
being at time 4,
B= 0.40, SE =.11, p < .001
. Interestingly,
stress at time 2 negatively predicted well-being at time 4,
B=−1.15, SE =.23, p < .001
. None of the remaining 15
variables were significant.
In the fourth model, we used productivity at time 4 as
outcome and all 17 variables listed in Table II measured at
Fig. 2. Productivity across time. The red line displays the trend over time,
whereas the box at each time point shows the range in which the middle 50%
of the data falls. A productivity score of one indicates that productivity has
not changed compared to pre-pandemic levels, scores
>
1 that productivity
increased and scores of <1 that productivity decreased.
time 2 as predictors. The overall model was not significant,
R=.17, adj.R2=.04, F (17,116) = 1.37, p =.17.
In the fifth model, we used well-being as outcome and all
17 variables measured at time 3 as predictors. The overall
model was significant,
R2= 52, adj.R2=.43, F (17,88) =
5.58, p < .001
. Non-surprisingly, well-being at time 3 predicted
well-being at time 4,
B= 0.38, SE =.13, p =.005
. However,
none of the other variables was significant.
In the sixth and final model, we used productivity at time 4 as
outcome and all 17 variables measured at time 3 as predictors.
The overall model was significant,
R2=.37, adj.R2=
.24, F (17,86) = 2.96, p < .001
. Productivity at time 3 pre-
dicted productivity at time 4, B= 0.46, SE =.11, p < .001.
C. Between-group comparisons
Finally, we compared women and men (results are summa-
rized in Table III) and people living in the United Kingdom
and the USA in Table IV (these were the two countries from
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 7
Fig. 3. Self-blame across time. The red line displays the trend over time,
whereas the box at each time point shows the range in which the middle 50%
of the data falls. Responses were given on a 5-point scale.
Fig. 4. Emotional loneliness across time. The red line displays the trend over
time, whereas the box at each time point shows the range in which the middle
50% of the data falls. Responses were given on a 5-point scale.
which most of our participants came) across all 17 variables
and all 4 time points. At time 1, our sample consisted of
37 women and 154 men, and 63 people were living in the
UK and 52 in the USA. At time 4, 27 women and 96 men
remained, as well as 39 people living in the UK and 30 in the
USA. However, none of the 68 between-gender comparisons
reached statistical significance at
α=.0005
, all
ps > .003
.
Instead, similarities between groups were large, PCR = 91.65,
range = 75.20 −99.96.
Also, none of the 68 between-country comparisons reached
statistical significance, all
ps > .04
. Instead, similarities be-
tween groups were large, PCR = 94.82,
range = 82.82−99.60
.
V. DISCUSSION
Building on the collected evidence and the previous literature,
we discuss the implications of our investigation for software
professionals and organizations. Furthermore, we explain the
intrinsic limitations of this study and how we tried to cope
with those.
The readers should be aware that our findings are based
on group-level inferences, which do not always generalize to
Fig. 5. Quality of social contacts across time. The red line displays the trend
over time, whereas the box at each time point shows the range in which the
middle 50% of the data falls. Responses were given on a 6-point scale.
the individual level. For example, the results of the within-
subject ANOVAs inform us whether the average of a variable
changed over time, not whether all individuals changed in the
same direction. As can be seen in Table II, while the well-
being of 77 developers increased between time 1 and 4, the
well-being of 38 developers dropped. Thus, it is only more
likely (i.e., approximately twice as likely) to find a developer
whose well-being increased instead of dropped. Interestingly,
the change over time was not always linear. For example,
emotional loneliness first went down between Time 1 and Time
2, then slightly up again at Time 3, and finally down again. This
might be because many countries started to (announce plans to)
open up again around the time when we collected the second
wave. In contrast, the third wave was collected in February
2021: In the UK and USA, for example, in the winter 2020/21
the deaths of many more people was associated with COVID-19
compared to spring 2020. Similarly, many situational factors or
variables we have not measured, such as the perceived severity
of local lockdowns or loss of a loved one (e.g., because of
COVID-19), would likely have explained additional variance
in developers’ well-being and productivity. Nevertheless, we
aimed to provide generalizable evidence with this longitudinal
study. However, qualitative investigations (e.g., [
41
], [
40
], [
42
])
add to a nuanced understanding of individual phenomena. When
drawing company guidelines, these and other studies should
also be considered since our recommendations will not be
exhaustive.
A. Implications
Based on our results, we provide recommendations for the
software engineering community (cf. also Table V).
We found that developers’
well-being increased over time
.
We have no pre-pandemic data, so we can not assess how the
lockdown initially impacted software professionals. It could
be that their well-being went down in Spring 2020 and is
now bouncing back to pre-pandemic times. This reasoning
would be in line with previous research showing that people’s
well-being usually bounces back after a significant negative
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 8
TABLE III
COM PARI SON S BE TWE EN WO ME N AND M EN
Wave 1 Wave 2
Men M Men SD Women M Women SD t-value p-value Cohen’s d PCR Men M Men SD Women M Women SD t-value p-value Cohen’s d PCR
Well-being 4.109 1.336 4.263 1.495 -0.581 0.5639 -0.113 95.494 4.388 1.258 4.151 1.407 0.933 0.3553 0.183 92.71
Productivity 1.008 0.416 0.917 0.43 1.18 0.243 0.218 91.32 1.029 0.433 1.043 0.452 -0.175 0.862 -0.033 98.684
Boredom 2.942 1.13 2.908 1.179 0.163 0.8713 0.03 98.803 2.923 1.122 2.943 1.309 -0.082 0.9353 -0.016 99.362
Behavioral disengagement 1.799 0.935 1.829 0.953 -0.176 0.8611 -0.032 98.723 1.997 0.957 2.324 1.259 -1.479 0.1458 -0.32 87.288
Self blame 1.753 0.957 2.053 1.095 -1.546 0.1283 -0.304 87.919 1.83 0.955 2.081 1.211 -1.173 0.2465 -0.248 90.132
Relatedness 3.483 0.801 3.557 0.948 -0.446 0.6577 -0.089 96.451 2.459 0.869 2.378 1.003 0.449 0.655 0.09 96.411
Competence 3.566 0.704 3.596 0.862 -0.202 0.8407 -0.041 98.364 2.098 0.963 2.475 1.144 -1.844 0.0712 -0.376 85.088
Autonomy 3.476 0.668 3.509 0.771 -0.239 0.8119 -0.047 98.125 1.975 0.837 2.135 0.998 -0.899 0.373 -0.184 92.67
Communication 4.511 1.004 4.623 0.972 -0.625 0.5343 -0.112 95.534 2.56 0.985 2.577 1.151 -0.08 0.9365 -0.016 99.362
Stress 2.468 0.744 2.638 1.025 -0.966 0.3391 -0.212 91.558 3.56 0.748 3.554 1.004 0.034 0.9728 0.008 99.681
Daily routines 4.758 1.469 4.368 1.877 1.191 0.2394 0.25 90.052 3.605 0.692 3.491 0.874 0.74 0.4628 0.156 93.783
Extraversion 3.401 0.786 3.638 0.766 -1.7 0.0944 -0.303 87.958 3.526 0.698 3.45 0.863 0.494 0.6236 0.103 95.893
Distractions 2.481 0.884 2.408 1.126 0.37 0.7127 0.078 96.889 4.281 1.055 4.432 1.165 -0.719 0.4754 -0.14 94.419
Generalized anxiety 2.123 0.916 2.738 1.175 -3.007 0.0042 -0.632 75.2 4.293 1.118 4.288 1.434 0.02 0.9839 0.004 99.84
Emotional loneliness 2.022 0.848 2.474 1.033 -2.498 0.0158 -0.51 79.872 2.485 0.748 2.662 0.965 -1.042 0.3025 -0.223 91.122
Social loneliness 2.667 0.969 2.535 1.146 0.653 0.5169 0.131 94.778 4.902 1.439 3.982 1.689 3.049 0.0037 0.617 75.77
Social contacts 4.032 1.068 4.421 1.149 -1.893 0.0637 -0.358 85.794 3.405 0.775 3.662 0.769 -1.817 0.0745 -0.333 86.776
Wave 3 Wave 4
Men M Men SD Women M Women SD t-value p-value Cohen’s d PCR Men M Men SD Women M Women SD t-value p-value Cohen’s d PCR
Well-being 4.456 1.418 4.207 1.576 0.787 0.4356 0.172 93.147 4.718 1.479 4.63 1.341 0.294 0.7698 0.061 97.567
Productivity 1.057 0.413 1.116 0.555 -0.542 0.5912 -0.132 94.738 1.057 0.413 1.116 0.555 -0.542 0.5912 -0.132 94.738
Boredom 2.786 1.253 3 1.361 -0.778 0.4408 -0.168 93.306 2.774 1.163 2.741 1.243 0.126 0.9 0.029 98.843
Behavioral disengagement 1.798 1.057 2.183 1.263 -1.535 0.1327 -0.349 86.147 1.799 1.082 1.981 1.014 -0.815 0.4195 -0.171 93.186
Self blame 2.167 1.208 2.7 1.495 -1.805 0.0787 -0.419 83.406 2.119 1.224 2.741 1.296 -2.232 0.0313 -0.502 80.181
Distractions 2.408 0.921 2.417 1.099 -0.04 0.9682 -0.009 99.641 2.381 0.946 2.352 0.83 0.159 0.8745 0.032 98.723
Generalized anxiety 2.099 1.005 2.605 1.245 -2.055 0.0466 -0.478 81.111 2.012 1.027 2.397 1.133 -1.592 0.1194 -0.366 85.48
Emotional loneliness 2.05 0.883 2.311 1.017 -1.286 0.2055 -0.287 88.59 1.845 0.904 2.012 0.908 -0.846 0.4025 -0.185 92.63
Social loneliness 2.789 1.071 2.789 1.153 0.003 0.998 0.001 99.96 2.708 1 2.827 1.178 -0.48 0.634 -0.115 95.415
Relatedness 3.554 0.793 3.2 0.952 1.873 0.0684 0.428 83.055 3.624 0.828 3.481 0.814 0.8 0.4283 0.172 93.147
Competence 3.664 0.716 3.444 0.892 1.245 0.2204 0.29 88.471 3.732 0.684 3.426 0.887 1.66 0.1057 0.418 83.445
Autonomy 3.488 0.745 3.144 0.807 2.11 0.0407 0.454 82.042 3.522 0.789 3.488 0.72 0.217 0.8295 0.045 98.205
Social contacts 4.099 1.085 3.944 1.26 0.616 0.5411 0.138 94.499 4.323 1.09 4.012 1.259 1.166 0.2509 0.275 89.064
Communication 4.444 1.171 4.402 1.373 0.152 0.8803 0.035 98.604 4.372 1.199 4.407 1.221 -0.132 0.8956 -0.029 98.843
Stress 2.471 0.858 2.717 0.96 -1.272 0.2104 -0.279 88.906 2.392 0.885 2.63 0.708 -1.457 0.1513 -0.28 88.866
Daily routines 5.029 1.434 4.078 1.875 2.588 0.0136 0.62 75.656 5.003 1.446 4.148 1.884 2.186 0.0356 0.552 78.255
Extraversion 3.454 0.766 3.517 0.94 -0.337 0.7377 -0.078 96.889 3.443 0.727 3.528 0.663 -0.573 0.5692 -0.118 95.295
TABLE IV
COMPARISONS BETWEEN DEVELOPERS BASED IN THE UNITED KINGDOM AND UNITED STATES O F AMERICA
Wave 1 Wave 2
UK M UK SD US M US SD t-value p-value Cohen’s d PCR UK M UK SD US M US SD t-value p-value Cohen’s d PCR
Well-being 4.248 1.302 4.288 1.448 -0.158 0.8752 -0.03 98.803 4.294 1.22 4.392 1.461 -0.381 0.7039 -0.074 97.049
Productivity 1.018 0.453 0.936 0.385 1.047 0.2975 0.193 92.312 0.977 0.414 1.076 0.472 -1.162 0.248 -0.225 91.043
Boredom 2.857 1.072 2.889 1.194 -0.151 0.8802 -0.029 98.843 2.96 1.159 2.74 1.166 0.994 0.3226 0.189 92.471
Behavioral disengagement 1.865 0.885 1.683 0.852 1.123 0.2641 0.21 91.638 2.089 0.952 1.91 1.024 0.948 0.3456 0.182 92.749
Self blame 1.786 0.932 1.74 1.059 0.241 0.81 0.046 98.165 1.944 1.025 1.68 0.896 1.451 0.1498 0.272 89.182
Relatedness 3.521 0.772 3.545 0.827 -0.158 0.875 -0.03 98.803 2.54 0.816 2.5 0.985 0.232 0.8168 0.045 98.205
Competence 3.569 0.734 3.593 0.873 -0.159 0.8742 -0.03 98.803 2.219 0.965 1.989 0.962 1.257 0.2114 0.239 90.488
Autonomy 3.503 0.7 3.516 0.754 -0.098 0.9223 -0.018 99.282 2.059 0.938 1.887 0.796 1.052 0.2949 0.197 92.154
Communication 4.472 1.031 4.593 1 -0.624 0.5341 -0.119 95.255 2.527 0.942 2.493 1.135 0.168 0.8673 0.032 98.723
Stress 2.528 0.713 2.312 0.871 1.43 0.156 0.273 89.143 3.538 0.711 3.593 0.847 -0.372 0.7111 -0.072 97.128
Daily routines 4.889 1.409 4.474 1.738 1.385 0.1693 0.265 89.459 3.605 0.67 3.65 0.815 -0.315 0.7534 -0.061 97.567
Extraversion 3.552 0.728 3.486 0.799 0.459 0.6473 0.087 96.53 3.565 0.751 3.443 0.818 0.808 0.421 0.155 93.823
Distractions 2.532 0.92 2.385 1.018 0.806 0.4222 0.152 93.942 4.274 1.12 4.327 1.189 -0.238 0.8121 -0.046 98.165
Generalized anxiety 2.265 0.942 2.134 1.075 0.689 0.4926 0.131 94.778 4.383 1.237 4.245 1.263 0.573 0.5678 0.11 95.614
Emotional loneliness 2.048 0.956 2.038 0.802 0.056 0.9555 0.01 99.601 2.573 0.69 2.34 0.89 1.516 0.133 0.296 88.234
Social loneliness 2.619 0.912 2.583 1.074 0.19 0.8498 0.036 98.564 4.817 1.531 4.647 1.646 0.562 0.5752 0.108 95.694
Social contacts 4.053 1.091 4.218 1.064 -0.818 0.415 -0.153 93.902 3.516 0.742 3.53 0.798 -0.094 0.925 -0.018 99.282
Wave 3 Wave 4
UK M UK SD US M US SD t-value p-value Cohen’s d PCR UK M UK SD US M US SD t-value p-value Cohen’s d PCR
Well-being 4.508 1.18 4.611 1.616 -0.323 0.7481 -0.074 97.049 4.751 1.325 4.733 1.554 0.051 0.9596 0.013 99.481
Productivity 1.103 0.511 1.081 0.439 0.211 0.8331 0.046 98.165 1.103 0.511 1.081 0.439 0.211 0.8331 0.046 98.165
Boredom 2.865 1.135 2.413 1.148 1.792 0.0772 0.396 84.305 2.79 1.161 2.558 1.218 0.806 0.4234 0.195 92.233
Behavioral disengagement 1.875 1.137 1.722 1.168 0.6 0.5502 0.133 94.698 1.817 1.053 1.833 1.155 -0.061 0.9517 -0.015 99.402
Self blame 2.271 1.12 1.889 1.321 1.398 0.1665 0.316 87.446 2.305 1.298 2.117 1.112 0.656 0.5141 0.154 93.862
Distractions 2.354 0.844 2.25 0.914 0.534 0.595 0.119 95.255 2.329 0.826 2.367 0.964 -0.171 0.8646 -0.042 98.325
Generalized anxiety 2.193 1.006 2.095 1.137 0.411 0.682 0.092 96.331 2.024 0.963 2.048 1.175 -0.089 0.9297 -0.022 99.122
Emotional loneliness 2.083 0.86 2.028 0.977 0.271 0.787 0.061 97.567 1.959 0.895 1.744 0.87 1.016 0.3135 0.243 90.33
Social loneliness 2.674 0.903 2.491 1.197 0.768 0.4456 0.176 92.988 2.553 0.89 2.722 1.135 -0.679 0.5002 -0.169 93.266
Relatedness 3.479 0.764 3.718 0.799 -1.379 0.1719 -0.306 87.84 3.65 0.714 3.689 0.882 -0.197 0.8449 -0.049 98.045
Competence 3.632 0.643 3.764 0.79 -0.819 0.4158 -0.186 92.59 3.65 0.694 3.672 0.839 -0.116 0.9079 -0.029 98.843
Autonomy 3.403 0.716 3.523 0.832 -0.696 0.4888 -0.157 93.743 3.614 0.706 3.567 0.711 0.277 0.7827 0.067 97.328
Social contacts 4.014 1.034 4.38 1.042 -1.597 0.1144 -0.353 85.99 4.374 1.123 4.322 1.049 0.199 0.8426 0.047 98.125
Communication 4.299 1.283 4.81 1.011 -2.028 0.0459 -0.434 82.821 4.275 1.322 4.444 0.996 -0.612 0.5428 -0.142 94.34
Stress 2.547 0.738 2.403 0.955 0.753 0.4545 0.172 93.147 2.409 0.784 2.35 0.829 0.301 0.7648 0.073 97.088
Daily routines 4.944 1.6 5.009 1.552 -0.187 0.8522 -0.041 98.364 5.122 1.533 4.756 1.744 0.92 0.3615 0.226 91.003
Extraversion 3.568 0.766 3.576 0.843 -0.049 0.9614 -0.011 99.561 3.591 0.72 3.517 0.707 0.437 0.6636 0.105 95.813
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 9
event [
72
]. While research from the start of the pandemic (i.e.,
spring 2020) indicates that developers’ well-being decreased
initially [
4
], our findings provide a more positive outlook that
developers’ well-being bounced back. Our findings also suggest
that working from home does not negatively impact developers’
well-being, as otherwise well-being would not have increased
as much. This supports new company policies implementing
hybrid or full remote work settings.
Productivity
remained constant during the pandemic. Al-
though we report a slight increase in productivity over the
four data collection waves (as plotted in Figure 2), and more
people reported an increase in productivity compared to those
who reported a decrease (cf. Table II), the mean differences
are non-significant (
p=.13
), indicating that the observed
increase could very well be random and might not replicate.
Since measuring productivity is non-trivial, we followed a
previous study example [
8
] by measuring productivity as a
self-reported function compared to the pre-pandemic period.
We therefore conclude that the productivity level of software
professionals did not change not only throughout the lockdown
but also compared to the pre-pandemic time. This finding also
contradicts previous research suggesting that the lockdown is
detrimental for productivity [
48
], possibly because of differ-
ences in the research design (cross-sectional vs. longitudinal
design) and operationalization of productivity (Ralph et al. [
48
]
used a different measure of productivity). Alternatively (or
additionally), we collected our sample approximately one
months after Ralph et al. [
48
] and predominantly from countries
which were relatively underrepresented in the sample of Ralph
et al., who recruited most of their participants from Germany,
Russia, and Brazil. Our results substantiate our previous
conclusion that a hybrid or full remote working environment
would not per se harm the productivity levels of developers.
Even though all typical welfare support (e.g., childcare,
schools, sports facilities) was closed, software engineers showed
a high level of adaptation by keeping the same productivity lev-
els and steadily increasing their well-being levels. Consequently,
in a post-pandemic working from home context, with all
support facilities normally running, working from home is very
unlikely impacted developers’ well-being negatively. Qualitative
findings support this argument, suggesting that working from
home significantly improved work-life balance [
40
]. Similarly,
a large-scale cross-sectional study observed that
89
% of
the surveyed professionals would like to continue to work
remotely (especially in a hybrid fashion), also in the future to
come [
73
]. However, previous research regarding the impact
of working from home on productivity is mixed. Some studies
found that working from home is positively or unrelated to
productivity [
47
], [
74
], [
75
], [
8
], whereas other research found
that working from home has some negative effects [
76
], [
77
],
[78]).
Software professionals
felt less lonely and improved
their social contacts
. During the first lockdown in Spring
2020, many people had to abruptly reduce their social in-
teractions [
79
]. As a consequence, this increased the sense
of loneliness and isolation. Nevertheless, also, in this case,
developers showed a high level of resilience. Indeed, we report
a significant decrease in emotional loneliness and an increase in
the quality of social contact. This means that software engineers
increasingly reached out to their social contacts when they felt
lonely, thereby coping well with the challenging conditions
of the pandemic. Similarly, the quality of their relationships
increased. This is important because having a reliable social
support network is an essential coping mechanism, especially
in hard times and in moments of high stress [80], [81].
These findings are relevant for organizations planning to
implement a hybrid or remote work policy. Software engi-
neers showed a high level of resilience when coping with
unexpected events. At the same time, their social network was
a crucial support while working from home. This insight is
also supported by previous research, where communication
was found a relevant predictor for developers’ satisfaction
during the lockdown [
41
]. Consequently, a proactive company
policy of employees’ inclusion would sustain their well-being
levels. This would require a particular effort from the middle
management (because they are the direct company interface for
each employee) to ensure that every team member can express
herself and maintain stimulating and nurturing relationships
with their peers, since even interacting with weak social ties
(i.e., acquaintances) can improve people’s well-being [82].
Moreover, we also found that
self-blame increased
. This
finding was unexpected and might relate to the phenomenon
known as survivor’s guilt [
83
], which has been observed, for
example, among caretakers of cancer patients and is positively
associated with remorse [
84
]. We speculate that self-blame is
positively associated with survivor’s guilt (e.g., of not having
been affected by COVID-19) and remorse and might be stronger
among those developers who experienced loss (e.g., a relative
who died because of COVID-19). Mindful organizations might
offer employees psychological support to address the guilt and
remorse specifically.
Stress is our only significant factor detrimental to well-
being
. Although this result is not surprising per se, it is critical.
It provides evidence that stress and stress factors, in general,
were the most significant harm when working from home during
the pandemic. Therefore, to effectively sustain the employees’
well-being, it should be a company priority to reduce stress
levels. There are different approaches that organizations can im-
plement to tackle this crucial issue. According to Halpern [
85
],
flexibility has been very effective in reducing work-related
stress for both men and women. Moreover, a high level of
flexibility leads to high employee commitment which reduced
organization costs and missed deadlines. Similarly, Coetzer
and Rothmann found that a high level of organizational control
over employees’ tasks was negatively related to organizational
commitment and stress. Pay structure and job insecurity were
also found to be highly stressful for knowledge workers [
86
].
On this aspect, career management along with professional
expectation management are considered to be critical pillars
of human resource management to decrease stress levels [
87
].
Additionally, mindful practices, also in the workplace or at
home, can reduce stress and enhance sleep quality [
88
]. Overall,
organizations have several tools to reduce developers’ stress
by providing a high degree of autonomy in both task and work
schedules and manage fair and transparent expectations (along
with job stability and fair pay).
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 10
We have not observed differences between women and
men
. No significant mean differences were found between men
and women in all surveyed variables. This suggests that the
lockdown did not affect one gender more in our sample. This is
surprising since abundant other research found that women are
more impacted by the pandemic, especially career-wise [
89
],
[
90
]. For instance, a recent Brazilian cross-sectional survey
study concluded that women suffered more the lockdown due
to a higher involvement in housekeeping duties compared
to men [
45
]. In contrast, we found a very high level of
similarity between genders. We believe that this discrepancy
arises because the women in our sample are not representative.
For example, an inclusion criterion of ours was that they work
at least 20h/week. So we have a very specific group of women
in our sample (probably with fewer kids or with someone who
takes care of their kids so that they can work). This result
is also encouraging because similarities between women and
men, even if not representative, can increase women’s sense
of belonging and a higher likelihood to pursue a career in
a men-dominated field [
91
]. Also, for this reason, software
companies should not use gender-biased communication when
offering home support, implying that most works at home are
on women.
We found no mean differences between the UK and
USA
. We found high levels of similarities in how software
professionals were impacted in the USA and the UK. This
might be the result of the reliance of national health authorities
on the World Health Organization, making lockdown measures
fairly uniform between both countries. Consequently, global
software companies can homogeneously plan policies in case of
a future disastrous event across countries. However, they should
take individual differences into account, as in both countries,
developers are reporting higher well-being and productivity
and those reporting lower well-being and productivity (i.e.,
the within-country variability outweighed the between-country
variability). Also, we have no evidence suggesting that there
should be any difference between the USA and the UK in
working from home policies.
B. Limitations
In the following section, we discuss the most relevant
limitations of this work.
Reliability. For this investigation, we employed a four
wave longitudinal design. Informants have been identified
through a multi-stage selection screening to ensure they were
representative of the software engineering population. Also, we
computed an a priori power analysis to identify the minimum
number of participants required to provide reliable conclusions.
The internal consistencies (i.e., Cronbach’s
α
) ranged from
satisfactory to very good.
Construct validity. For this study we used
15
variables
previously identified in the literature that are related to well-
being and productivity. For any variable, we used a dedicated
measurement instrument. Construct validity was assessed by
correlating all variables with each other, separately in each
wave. The correlations were in the expected directions and in
line with the literature [70], [8], [71].
Conclusion validity. We draw the conclusions based on a
number of statistical analyses: within-subject ANOVA, cross-
lagged panel model, and between-subject t-tests. To increase the
trustworthiness of our results, we adjusted our alpha-thresholds
to reduce the risk of false positives (i.e., Type I errors). In
terms of data collection, some variations might have been out
of our control since lockdown measures were not uniform in
different countries. To address this issue, we only selected
participants living in countries that during the first wave had
similar regulations (we excluded, e.g., Sweden, Denmark).
Nevertheless, minor variations in terms of rules happened
during the pandemic in the different countries we had no
control over. However, we report very similar results when
looking at between-country mean differences. Our conclusions
are reproducible since we made the anonymized raw data and
R analysis code openly available on Zenodo.
Internal validity. We only found one instance in which one
predictor (stress) causally predicted an outcome (well-being)
over time. This might be because of our conservative approach
(e.g., correcting for multiple comparisons and controlling for
many other related variables). Of importance, we recognize
that there is an ongoing debate on what constitutes causality.
Therefore, we are aware that some readers might dislike the
term ‘causality’ and prefer instead a ‘softer’ term such as
‘predicted over time. Our study relies on self-reported measures,
limiting the validity due to potential response biases. Although
our informants have been initially identified in other work [
50
],
we applied several quality checks also after each time point.
Additionally, we searched for inaccurate or unlikely responses
(of which we found none, which ensures data quality). The
attrition rate across the four waves is comparable to other
longitudinal studies across a similar timespan [
93
], [
94
]. Due
to the evolving nature of the pandemic, data collection has been
performed based on the information available by that point in
time. As a consequence, the time spans are not homogeneous
but represent moments of the pandemic where data collection
seemed to be representative of the pandemic trend. This might
have affected the variability of our data.
External validity. The primary aim of our longitudinal
analysis was to maximize internal validity by finding significant
effects. Thus, we did not look to work with a representative
sample of the software engineering population (e.g., such
as Russo & Stol did with
N≈500
to generalize their
findings [50]).
VI. CONCLUSION
In this investigation, we performed a four-wave longitudinal
study over 14 months from the start of the COVID-19 pandemic
in April 2020 to July 2021, involving 192 software developers.
We analyzed how well-being and productivity of software
engineers and 15 related social and psychological variables
changed over time. Similarly, we explored causal relations
among our variables and performed gender and country-based
between-group comparisons.
We found that well-being, quality of social contacts, and self-
blame increased over time while emotional loneliness decreased.
We further found that only stress measured at time 2 causally
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 11
TABLE V
SUMMARY OF KEY FINDINGS &RECOMMENDATIONS
Findings Recommendations
Developers’ well-being in-
creased
Well-being consistently increased across all four time
points, indicating that they bounced back from the
negative impact the pandemic likely had on their
well-being initially.
Developers showed a high level of resilience when working from home
and improved their well-being. Software companies can extensively
implement (hybrid) working from home practices.
Productivity remained un-
changed
Developers’ productivity has not changed across all
four time points.
Working from home is not per se detrimental for productivity. If
organizations keep reasonable work expectations, professionals will be
as productive at home as in the office.
Developers felt less lonely
and improved their social
contacts
This suggests that developers managed to reduce their
loneliness, presumably by improving the quality and
quantity of their social interactions.
Active inclusion policies should be set in place for employees
working from home. Mainly middle-management should focus on
individual employees performance and their level of integration and
communication with the team.
Stress decreases well-
being levels
Stress at time 2 negatively predicted well-being at
time 4. This suggests that stress can have a long-
lasting impact on developers’ well-being.
Reducing professionals’ stress levels should be the key priority of every
organization. Practices such as flexibility, clear expectation, career
management, transparent and fair pay structure, as well as mindfulness
exercises can be effective.
Self-blame increased Levels of self-blame increased over time.
Software organizations might offer to employees psychological support
to investigate the reasons for self-blame.
Men and women are sim-
ilar across all measured
variables
This is in line with the gender similarity hypothe-
sis [
92
] that women and men are across most variables
(e.g., well-being related, ability, personality) more
similar than different.
When planning for home-support policies, organization should not
use biased communication implying that women are most affected.
This can increase the feeling of fitting in, which in turn can increase
girls’ and women’s intention to pursue a career in a men-dominated
field [91].
No country difference
(USA vs UK) when
dealing with the pandemic
Our findings indicate that people living in the UK
and the USA were impacted and ‘recovered’ from
the initial shock of the pandemic to a similar extent.
Especially during another disastrous event, organizations can plan the
same remote work strategies across both countries.
predicted well-being at time 4. Finally, we found that women
and men and people living in the UK and USA did not differ
for any of the variables we measured across all four data
collection waves.
The significance of our conclusions lies in the extensiveness
of our investigation (i.e., over one year) during most of the
COVID-19 pandemic (as of 2021). We carefully selected our
informants after an a priori power analysis to ensure the
trustworthiness of our results and adjusted our alpha level
to avoid false-positive results and misleading recommendations.
So far, this is the most complete longitudinal analysis involving
software engineers to understand the effects of the COVID-19
pandemic on their well-being and productivity. Moreover, our
results are relevant in case of another disastrous event, but
they also help the software engineering community to provide
better-informed recommendations for future Working from
Home policies after the pandemic.
Future works will therefore focus on a prolonged assessment
of the working conditions of our pool even after the pandemic.
Also, more nuanced understandings of phenomena we could
not explain (i.e., increased behavioral disengagement at time 2)
is necessary to include more relevant variables to understand
the underlying mechanisms or qualitative research designs, for
example.
SUP PL EM EN TARY MATERIA LS
The complete replication package is openly available under
CC BY 4.0 license on Zenodo, DOI: https://doi.org/10.5281/
zenodo.5713923.
ACK NOW LE DG ME NT
This work was supported by the Carlsberg Foundation under
grant agreement number CF20-0322 (PanTra — Pandemic
Transformation).
REFERENCES
[1]
S. K. Brooks, R. K. Webster, L. E. Smith, L. Woodland, S. Wessely,
N. Greenberg, and G. J. Rubin, “The psychological impact of quarantine
and how to reduce it: rapid review of the evidence,” The Lancet, vol.
395, 2020.
[2]
M. Lipsitch, D. L. Swerdlow, and L. Finelli, “Defining the epidemiology
of Covid-19 — studies needed,” New England Journal of Medicine, vol.
382, no. 13, pp. 1194–1196, 2020.
[3]
R. Farmer and N. D. Sundberg, “Boredom proneness–the development
and correlates of a new scale,” Journal of Personality Assessment, vol. 50,
no. 1, pp. 4–17, 1986.
[4]
P. Ralph et al., “Pandemic programming: How COVID-19 affects software
developers and how their organizations can help,” Empirical Software
Engineering, 2020.
[5]
N. Donnelly and S. B. Proctor-Thomson, “Disrupted work: home-based
teleworking (hbtw) in the aftermath of a natural disaster,” New Technology,
Work and Employment, vol. 30, no. 1, pp. 47–61, 2015.
[6]
C. Krekel, G. Ward, and J.-E. De Neve, “Employee wellbeing,
productivity, and firm performance,” Sa
¨
ıd Business School WP, vol. 4,
2019. [Online]. Available: http://eureka.sbs.ox.ac.uk/7348/1/2019-04.pdf
[7]
S. Carolan, P. R. Harris, and K. Cavanagh, “Improving employee well-
being and effectiveness: systematic review and meta-analysis of web-
based psychological interventions delivered in the workplace,” Journal
of Medical Internet Research, vol. 19, no. 7, p. e271, 2017.
[8]
D. Russo, P. H. P. Hanel, S. Altnickel, and N. van Berkel, “Predictors
of well-being and productivity among software professionals during
the COVID-19 pandemic–a longitudinal study,” Empirical Software
Engineering, vol. 26, no. 62, pp. 1–64, 2021.
[9]
F. Herzberg, B. Mausner, and B. B. Snyderman, Motivation to work.
London: Transaction Publishers, 2017.
[10]
R. M. Ryan and E. L. Deci, “Self-determination theory and the facilitation
of intrinsic motivation, social development, and well-being.” American
Psychologist, vol. 55, no. 1, pp. 68–78, 2000.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 12
[11]
M. Gagn
´
e and E. L. Deci, “Self-determination theory and work
motivation,” Journal of Organizational behavior, vol. 26, no. 4, pp.
331–362, 2005.
[12]
L. Lascau, S. J. J. Gould, A. L. Cox, E. Karmannaya, and D. P. Brumby,
“Monotasking or multitasking: Designing for crowdworkers’ preferences,”
in Proceedings of the 2019 CHI Conference on Human Factors in
Computing Systems, ser. CHI ’19, New York, NY, USA, 2019.
[13]
A. J. Anderson, S. A. Kaplan, and R. P. Vega, “The impact of telework on
emotional experience: When, and for whom, does telework improve daily
affective well-being?” European Journal of Work and Organizational
Psychology, vol. 24, no. 6, pp. 882–897, 2015.
[14]
N. Bloom, J. Liang, J. Roberts, and Z. J. Ying, “Does working from
home work? evidence from a chinese experiment,” The Quarterly Journal
of Economics, vol. 130, no. 1, pp. 165–218, 2015.
[15]
NHS, “Mental wellbeing while staying at home,”
https://www.nhs.uk/oneyou/every-mind-matters/coronavirus-covid-
19-staying-at-home-tips/, 2020.
[16]
Danish Health Authority, “Questions and answers on novel
coronavirus,” https://www.sst.dk/da/Viden/Smitsomme-sygdomme/
Smitsomme-sygdomme- A-AA/Coronavirus/Spoergsmaal-og- svar/
Questions-and- answers, 2020.
[17]
CIPD, “Getting the most from remote working,” https:
//www.cipd.co.uk/knowledge/fundamentals/relations/flexible-working/
remote-working- top-tips, 2020.
[18]
P. D. Lunn, C. A. Belton, C. Lavin, F. P. McGowan, S. Timmons, and
D. A. Robertson, “Using behavioral science to help fight the coronavirus,”
Journal of Behavioral Public Administration, vol. 3, no. 1, 2020.
[19]
G. Sprang and M. Silman, “Posttraumatic stress disorder in parents and
youth after health-related disasters,” Disaster medicine and public health
preparedness, vol. 7, no. 1, pp. 105–110, 2013.
[20]
L. Hawryluck, W. L. Gold, S. Robinson, S. Pogorski, S. Galea, and
R. Styra, “Sars control and psychological effects of quarantine, toronto,
canada,” Emerging Infectious Diseases, vol. 10, no. 7, pp. 1206–1212,
2004.
[21]
S. Lee, L. Y. Chan, A. M. Chau, K. P. Kwok, and A. Kleinman, “The
experience of sars-related stigma at amoy gardens,” Social science &
medicine, vol. 61, no. 9, pp. 2038–2046, 2005.
[22]
Z. Marjanovic, E. R. Greenglass, and S. Coffey, “The relevance of
psychosocial variables and working conditions in predicting nurses’
coping strategies during the sars crisis: an online questionnaire survey,”
International journal of nursing studies, vol. 44, no. 6, pp. 991–998,
2007.
[23]
D. Reynolds, J. Garay, S. Deamond, M. Moran, W. Gold, and R. Styra,
“Understanding, compliance and psychological impact of the sars
quarantine experience,” Epidemiology & Infection, vol. 136, no. 7, pp.
997–1007, 2008.
[24]
Y. Bai, C.-C. Lin, C.-Y. Lin, J.-Y. Chen, C.-M. Chue, and P. Chou,
“Survey of stress reactions among health care workers involved with the
sars outbreak,” Psychiatric Services, vol. 55, no. 9, pp. 1055–1057, 2004.
[25]
Y. Kim, W. Zhong, M. Jehn, and L. Walsh, “Public risk perceptions and
preventive behaviors during the 2009 h1n1 influenza pandemic,” Disaster
Medicine and Public Health Preparedness, vol. 9, no. 2, pp. 145–154,
2015.
[26]
G. Prati, L. Pietrantoni, and B. Zani, “A social-cognitive model of
pandemic influenza h1n1 risk perception and recommended behaviors
in italy,” Risk Analysis: An International Journal, vol. 31, no. 4, pp.
645–656, 2011.
[27]
J. A. Wilken, P. Pordell, B. Goode, R. Jarteh, Z. Miller, B. G. Saygar,
L. Maximore, W. M. Borbor, M. Carmue, G. W. Walker et al.,
“Knowledge, attitudes, and practices among members of households
actively monitored or quarantined to prevent transmission of ebola virus
disease — Margibi County, Liberia: February-March 2015,” Prehospital
and Disaster Medicine, vol. 32, no. 6, pp. 673–678, 2017.
[28]
G. Caleo, J. Duncombe, F. Jephcott, K. Lokuge, C. Mills, E. Looijen,
F. Theoharaki, R. Kremer, K. Kleijer, J. Squire et al., “The factors
affecting household transmission dynamics and community compliance
with ebola control measures: a mixed-methods study in a rural village
in sierra leone,” BMC Public Health, vol. 18, no. 248, 2018.
[29]
Owl Labs, “State of remote work 2019,” Report, 2019. [Online].
Available: https://www.owllabs.com/state-of-remote- work/2019
[30]
R. P. Vega, A. J. Anderson, and S. A. Kaplan, “A within-person
examination of the effects of telework,” Journal of Business and
Psychology, vol. 30, no. 2, pp. 313–323, 2015.
[31]
Y. Baruch, “Teleworking: benefits and pitfalls as perceived by profes-
sionals and managers,” New Technology, Work and Employment, vol. 15,
no. 1, pp. 34–49, 2000.
[32]
W. F. Cascio, “Managing a virtual workplace,” Academy of Management
Perspectives, vol. 14, no. 3, pp. 81–90, 2000.
[33]
Buffer, “The 2020 state of remote work,” 2020. [Online]. Available:
https://lp.buffer.com/state-of-remote-work-2020
[34]
Gallup, “Is working remotely effective? Gallup research says yes,”
2020. [Online]. Available: https://www.gallup.com/workplace/283985/
working-remotely- effective-gallup-research-says-yes.aspx
[35]
C. Pounder, “Homeworking: No longer an easy option?” Computers &
Security, vol. 17, no. 1, pp. 27–30, 1998.
[36]
H. Guo, “Special requirements for software process improvement applied
in teleworking environments,” in Proceedings of the Second Asia-Pacific
Conference on Quality Software. IEEE, 2001, pp. 331–340.
[37]
K. Higa, O. R. L. Sheng, B. Shin, and A. J. Figueredo, “Understanding
relationships among teleworkers’e-mail usage, E-Mail richness percep-
tions, and E-Mail productivity perceptions under a software engineering
environment,” IEEE Transactions on Engineering Management, vol. 47,
no. 2, pp. 163–173, 2000.
[38]
P. James and D. Griffiths, “A secure portable execution environment to
support teleworking,” Information Management & Computer Security,
2014.
[39]
D. Ford, R. Milewicz, and A. Serebrenik, “How remote work can foster a
more inclusive environment for transgender developers,” in Proceedings of
the International Workshop on Gender Equality in Software Engineering.
IEEE, 2019, pp. 9–12.
[40]
D. Ford, M.-A. Storey, T. Zimmermann, C. Bird, S. Jaffe, C. Maddila,
J. L. Butler, B. Houck, and N. Nagappan, “A tale of two cities: Software
developers working from home during the COVID-19 pandemic,” arXiv
preprint arXiv:2008.11147, 2020.
[41]
C. Miller, P. Rodeghero, M.-A. Storey, D. Ford, and T. Zimmermann,
““how was your weekend?” software development teams working
from home during covid-19,” in International Conference on Software
Engineering. IEEE, 2021, pp. 624–636.
[42]
J. Butler and S. Jaffe, “Challenges and gratitude: A diary study of
software engineers working from home during covid-19 pandemic,” in
International Conference on Software Engineering. IEEE, 2021, pp.
362–363.
[43]
A.-A. Cucolas and D. Russo, “The impact of working from home on
the success of scrum projects: A multi-method study,” arXiv preprint
arXiv:2107.05955, 2021.
[44]
K. Cantarero, W. A. van Tilburg, and E. Smoktunowicz, “Affirming basic
psychological needs promotes mental well-being during the covid-19
outbreak,” Social Psychological and Personality Science, vol. 12, no. 5,
pp. 821–828, 2021.
[45]
L. S. Machado, C. Caldeira, M. Perin, and C. R. de Souza, “Gendered
experiences of software engineers during the covid-19 crisis,” IEEE
Software, vol. March-April, 2021.
[46]
V. M. Lamarche, “Socially connected and covid-19 prepared: The
influence of sociorelational safety on perceived importance of covid-19
precautions and trust in government responses.” Social Psychological
Bulletin, 2020.
[47]
L. Bao, T. Li, X. Xia, K. Zhu, H. Li, and X. Yang, “How does working
from home affect developer productivity?–a case study of baidu during
COVID-19 pandemic,” arXiv preprint arXiv:2005.13167, 2020.
[48]
P. Ralph et al., “Empirical standards for software engineering research,”
arXiv preprint arXiv:2010.03525, 2020.
[49]
F. Faul, E. Erdfelder, A. Buchner, and A.-G. Lang, “Statistical power
analyses using g* power 3.1: Tests for correlation and regression analyses,”
Behavior research methods, vol. 41, no. 4, pp. 1149–1160, 2009.
[50]
D. Russo and K.-J. Stol, “Gender differences in personality traits of
software engineers,” IEEE Transactions on Software Engineering, vol.
In Press, p. 16, 2020.
[51]
S. Palan and C. Schitter, “Prolific.ac—-a subject pool for online
experiments,” Journal of Behavioral and Experimental Finance, vol. 17,
pp. 22–27, 2018.
[52]
G. A. of the World Medical Association et al., “World medical association
declaration of helsinki: ethical principles for medical research involving
human subjects.” The Journal of the American College of Dentists, vol. 81,
no. 3, p. 14, 2014.
[53]
J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, R. L. Tatham et al.,
Multivariate data analysis. Pearson Education, 2013, vol. 7th ed.
[54]
E. Diener, R. A. Emmons, R. J. Larsen, and S. Griffin, “The satisfaction
with life scale,” Journal of personality assessment, vol. 49, no. 1, pp.
71–75, 1985.
[55]
S. Wagner and M. Ruhe, “A systematic review of productivity factors in
software development,” arXiv preprint arXiv:1801.06475, 2018.
[56]
A. J. Ko, “Why we should not measure productivity,” in Rethinking
Productivity in Software Engineering. Springer, 2019, pp. 21–26.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 13
[57]
A. N. Meyer, T. Fritz, G. C. Murphy, and T. Zimmermann, “Software
developers’ perceptions of productivity,” in Proceedings of the Interna-
tional Symposium on Foundations of Software Engineering, 2014, pp.
19–29.
[58]
A. A. Struk, J. S. Carriere, J. A. Cheyne, and J. Danckert, “A short
boredom proneness scale: Development and psychometric properties,”
Assessment, vol. 24, no. 3, pp. 346–359, 2017.
[59]
C. S. Carver, “You want to measure coping but your protocol’s too long:
Consider the brief COPE,” International Journal of Behavioral Medicine,
vol. 4, no. 1, p. 92, 1997.
[60]
R. L. Spitzer, K. Kroenke, J. B. Williams, and B. L
¨
owe, “A brief measure
for assessing generalized anxiety disorder: the gad-7,” Archives of internal
medicine, vol. 166, no. 10, pp. 1092–1097, 2006.
[61]
J. D. J. Gierveld and T. V. Tilburg, “A 6-item scale for overall, emotional,
and social loneliness: Confirmatory tests on survey data,” Research on
Aging, vol. 28, no. 5, pp. 582–598, 2006.
[62]
K. M. Sheldon and J. C. Hilpert, “The balanced measure of psychological
needs (bmpn) scale: An alternative domain general measure of need
satisfaction,” Motivation and Emotion, vol. 36, no. 4, pp. 439–451, 2012.
[63]
K. S. Birditt and T. C. Antonucci, “Relationship quality profiles and
well-being among married adults.” Journal of Family Psychology, vol. 21,
no. 4, pp. 595–604, 2007.
[64]
S. Cohen, “Perceived stress in a probability sample of the United States.”
The social psychology of health, 1988.
[65]
R. E. de Vries, “The 24-item brief HEXACO inventory (BHI),” Journal
of Research in Personality, vol. 47, no. 6, pp. 871 – 880, 2013.
[66]
J. M. Rohrer, “Thinking clearly about correlations and causation:
Graphical causal models for observational data,” Advances in Methods
and Practices in Psychological Science, vol. 1, no. 1, pp. 27–42, 2018.
[67]
C. W. Granger, “Testing for causality: a personal viewpoint,” Journal of
Economic Dynamics and control, vol. 2, pp. 329–352, 1980.
[68]
D. Rogosa, “A critique of cross-lagged correlation.” Psychological
Bulletin, vol. 88, no. 2, p. 245, 1980.
[69]
P. H. Hanel, G. R. Maio, and A. S. Manstead, “A new way to look at
the data: Similarities between groups of people are large and important.”
Journal of Personality and Social Psychology, vol. 116, no. 4, pp. 541–
562, 2019.
[70]
E. Diener, R. E. Lucas, and C. N. Scollon, “Beyond the hedonic
treadmill: Revising the adaptation theory of well-being,” in The
Science of Well-Being: The Collected Works of Ed Diener, ser. Social
Indicators Research Series, E. Diener, Ed. Springer. [Online]. Available:
10.1007/978-90- 481-2350- 6 5
[71]
G. Miller, “Why loneliness is hazardous to your health,” pp. 138–140,
2011.
[72]
A. J. Oswald and N. Powdthavee, “Does happiness adapt? a longitudinal
study of disability with implications for economists and judges,” Journal
of public economics, vol. 92, no. 5-6, pp. 1061–1077, 2008.
[73]
S. Walton, P. O’Kane, and D. Ruwhiu, “New Zealanders’ attitudes
towards working from home,” URL https://www.otago.ac.nz/news/news/
otago737417.html, University of Otago, Tech. Rep., 2020.
[74]
J. M. Barrero, N. Bloom, and S. J. Davis, “Why working from home
will stick,” https://www.nber.org/papers/w28731, National Bureau of
Economic Research, Tech. Rep., 2021.
[75]
S. S. Deole, M. Deter, and Y. Huang, “Home sweet home: Working from
home and employee performance during the covid-19 pandemic in the
uk,” https://papers.ssrn.com/sol3/papers.cfm?abstract id=3792236, Tech.
Rep., 2021.
[80]
N. Weinstein and R. M. Ryan, “A self-determination theory approach to
understanding stress incursion and responses,” Stress and Health, vol. 27,
no. 1, pp. 4–17, 2011.
[76]
M. Gibbs, F. Mengel, and C. Siemroth, “Work from home & productivity:
Evidence from personnel & analytics data on it professionals,” University
of Chicago, Becker Friedman Institute for Economics, Tech. Rep. 2021-
56, 2021.
[77]
R. Kitagawa, S. Kuroda, H. Okudaira, and H. Owan, “Working from
home: Its effects on productivity and mental health,” Research Institute
of Economy, Trade and Industry (RIETI, Tech. Rep., 2021.
[78]
M. Morikawa, “Productivity of working from home during the covid-
19 pandemic: Evidence from an employee survey (japanese),” https:
//econpapers.repec.org/paper/etirdpsjp/20034.htm, Research Institute of
Economy, Trade and Industry (RIETI), Tech. Rep., 2020.
[79]
H. F. Chan, J. W. Moon, D. A. Savage, A. Skali, B. Torgler, and S. Whyte,
“Can psychological traits explain mobility behavior during the covid-19
pandemic?” Social Psychological and Personality Science, vol. 12, no. 6,
pp. 1018–1029, 2021.
[81]
C. S. Carver, M. F. Scheier, and J. K. Weintraub, “Assessing coping
strategies: a theoretically based approach.” Journal of Personality and
Social Psychology, vol. 56, no. 2, pp. 267–283, 1989.
[82]
G. M. Sandstrom and E. W. Dunn, “Social interactions and well-being:
The surprising power of weak ties,” Personality and Social Psychology
Bulletin, vol. 40, no. 7, pp. 910–922, 2014.
[83]
S. P. Hutson, J. M. Hall, and F. L. Pack, “Survivor guilt,” Advances in
Nursing Science, vol. 38, no. 1, pp. 20–33, 2015.
[84]
S. Kreitler, F. Barak, Y. Alkalay, and N. Siegelman-Danieli, “Survivor’s
guilt in caretakers of cancer,” in The presence of the dead in our lives,
2012, pp. 77–98.
[85]
D. F. Halpern, “How time-flexible work policies can reduce stress,
improve health, and save money,” Stress and health, vol. 21, no. 3,
pp. 157–168, 2005.
[86] W. Coetzer and S. Rothmann, “Occupational stress of employees in an
insurance company,” South African Journal of Business Management,
vol. 37, no. 3, pp. 29–39, 2006.
[87]
M. London and E. M. Mone, Career management and survival in
the workplace: Helping employees make tough career decisions, stay
motivated, and reduce career stress. Jossey-Bass, 1987.
[88]
M. Klatt, C. Norre, B. Reader, L. Yodice, and S. White, “Mindfulness in
motion: A mindfulness-based intervention to reduce stress and enhance
quality of sleep in scandinavian employees,” Mindfulness, vol. 8, no. 2,
pp. 481–488, 2017.
[89]
K. Power, “The covid-19 pandemic has increased the care burden of
women and families,” Sustainability: Science, Practice and Policy, vol. 16,
no. 1, pp. 67–73, 2020.
[90]
A.-C. Pinho-Gomes, S. Peters, K. Thompson, C. Hockham, K. Ripullone,
M. Woodward, and C. Carcel, “Where are the women? gender inequalities
in covid-19 research authorship,” BMJ Global Health, vol. 5, no. 7, p.
e002922, 2020.
[91]
C. Good, A. Rattan, and C. S. Dweck, “Why do women opt out? sense
of belonging and women’s representation in mathematics.” Journal of
personality and social psychology, vol. 102, no. 4, p. 700, 2012.
[92]
J. S. Hyde, “The gender similarities hypothesis.” American psychologist,
vol. 60, no. 6, p. 581, 2005.
[93]
A. Bardi, K. E. Buchanan, R. Goodwin, L. Slabu, and M. Robinson,
“Value stability and change during self-chosen life transitions: Self-
selection versus socialization effects.” Journal of personality and social
psychology, vol. 106, no. 1, pp. 131–147, 2014.
[94]
M. Feinberg, C. Kovacheff, R. Teper, and Y. Inbar, “Understanding
the process of moralization: How eating meat becomes a moral issue.”
Journal of personality and social psychology, vol. 117, no. 1, p. 50,
2019.
