Predictors of Well-being and Productivity among Software Professionals during the COVID-19 Pandemic – A Longitudinal Study

07/24/2020 ∙ by Daniel Russo, et al. ∙ Aalborg University 0

The COVID-19 pandemic has forced governments worldwide to impose movement restrictions on their citizens. Although critical to reducing the virus' reproduction rate, these restrictions come with far-reaching social and economic consequences. In this paper, we investigate the impact of these restrictions on an individual level among software engineers currently working from home. Although software professionals are accustomed to working with digital tools, but not all of them remotely, in their day-to-day work, the abrupt and enforced work-from-home context has resulted in an unprecedented scenario for the software engineering community. In a two-wave longitudinal study (N = 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity. Examples include anxiety, distractions, psychological and physical needs, office set-up, stress, and work motivation. This design allowed us to identify those variables that explain unique variance in well-being and productivity. Results include (1) the quality of social contacts predicted positively, and stress predicted an individual's well-being negatively when controlling for other variables consistently across both waves; (2) boredom and distractions predicted productivity negatively; (3) productivity was less strongly associated with all predictor variables at time two compared to time one, suggesting that software engineers adapted to the lockdown situation over time; and (4) the longitudinal study did not provide evidence that any predictor variable causal explained variance in well-being and productivity. Our study can assess the effectiveness of current work-from-home and general well-being and productivity support guidelines and provide tailored insights for software professionals.

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1 Introduction

The mobility restrictions imposed on billions of people during the COVID-19 pandemic in the first half of 2020 successfully decreased the reproduction rate of the virus rocklov2020covid; world2020considerations. However, quarantine and isolation also come with tremendous costs on people’s well-being brooks2020 and productivity lipsitch2020defining

. For example, the psychosocial consequences of COVID-19 mitigation strategies have resulted in an estimated average loss of 0.2 years of life 

moser2020years.

While prior research brooks2020 has identified numerous factors either positively or negatively associated with people’s well-being during disastrous events, most of this research was cross-sectional and included a limited set of predictors. Further, whether productivity is affected by disastrous events and, if so, why precisely, has not yet been investigated in a peer-reviewed article to the best of our knowledge. This is especially relevant since many companies, including tech companies, have instructed their employees to work from home duffy2020cnn at an unprecedented scope. Thus, it is unclear whether previous research on remote work donnelly2015disrupted still holds during a global pandemic while schools are closed, and professionals often have to work in non-work dedicated areas of their homes. It is particularly interesting to study the effect of quarantine on software engineers as they are often already experienced in working remotely, which might help mitigate the adverse effects of the lockdown on their well-being and productivity. Therefore, there is a compelling need for longitudinal applied research that draws on theories and findings from various scientific fields to identify variables that uniquely predict the well-being and productivity of software professionals during the 2020 quarantine, for both the current and potential future lockdowns.

The software engineering community has never before faced such a wide-scale lockdown and quarantine scenario during the global spread of the COVID-19 virus. As a result, we can not build on pre-existing literature to provide tailored recommendations for software professionals. Accordingly, in the present research, we integrate theories from the organizational herzberg2017motivation and psychological masi2011meta; ryan2000self literature, as well as findings from research on remote work Lascau2019workers; anderson2015impact; bloom2015does and recommendations by health nhs_2020; sst_2020 and work CIPD authorities targeted at the general population. This longitudinal investigation provides the following contributions:

  • First, by including a range of variables relevant to well-being and productivity, we are able to identify those variables that are uniquely associated with these two dependent variables for software professionals and thus help improve guidelines and tailor recommendations.

  • Second, a longitudinal design allows us to explore which variables predict (rather than are predicted by) well-being and productivity of software professionals.

  • Third, the current mobility restrictions imposed on billions of people provide a unique opportunity to study the effects of working remotely on people’s well-being and productivity.

Our results are relevant to the software community because the number of knowledge workers who are at least partly working remotely is increasing gallup2020, yet the impact of working remotely on people’s health and productivity is not well understood yet mann2003psychological. We focus on well-being and productivity as dependent variables because both are crucial for our way of living. Well-being is a fundamental human right, according to the Universal Declaration of Human Rights, and productivity allows us to maintain a certain standard of living and thus also affects our overall well-being. Thus, our research question is:
Research Question: What are relevant predictors of well-being and productivity for software engineers who are working remotely during a pandemic?

In the remainder of this paper, we describe the related work about well-being in quarantine and productivity in remote work in Section 2, followed by a discussion about the research design of this longitudinal study in Section 3. The analysis is described in Section 4, and results discussed in Section 5. Implications and recommendations for software engineers, companies, and any remote-work interested parties is then outlined in Section 6. Finally, we conclude this study by outlying future research directions in Section 7.

2 Related Work

2.1 Well-Being in Quarantine

To slow down the spread of pandemics, it is often necessary to quarantine a large number of people rocklov2020covid; world2020considerations and enforce social distancing to limit the spread of the infection anderson2020will. This typically implies that only people working in essential professions such as healthcare, police, pharmacies, or food chains, such as supermarkets, are allowed to leave their homes for work. If possible, people are asked to work remotely from home. However, such measures are perceived as drastic and can have severe consequences on people’s well-being brooks2020; lunn2020using.

Previous research has found that being quarantined can lead to anger, depression, emotional exhaustion, fear of infecting others or getting infected, insomnia, irritability, loneliness, low mood, post-traumatic stress disorders, and stress sprang2013posttraumatic; hawryluck2004sars; lee2005experience; marjanovic2007relevance; reynolds2008understanding; bai2004survey. The fear of getting infected and infecting others, in turn, can become a substantial psychological burden kim2015public; prati2011social. Also, a lack of necessary supplies such as food or water wilken2017knowledge and insufficient information from public health authorities adds on to increased stress levels caleo2018factors. The severity of the symptoms correlated positively with the duration of being quarantined and symptoms can still appear years after quarantine has ended brooks2020. This makes it essential to understand what differentiates those whose mental health is more negatively affected by being quarantined from those who are less strongly affected. However, a recent review found that no demographic variable was conclusive in predicting whether someone would develop psychological issues while being quarantined brooks2020. Moreover, prior studies investigating such predictors focused solely on demographic factors (e.g., age or number of children hawryluck2004sars; taylor2008factors). This suggests that additional research is needed to identify psychological and demographic predictors of well-being. For example, prior research suggested that a lack of autonomy, which is an innate psychology need ryan2000self, negatively affects people’s well-being and motivation calvo2020health, yet evidence to support this claim in the context of a quarantine is missing.

To ease the intense pressure on people while being quarantined or in isolation, research and guidelines from health authorities provide a range of solutions on how an individual’s well-being can be improved. Some of these factors lie outside of the control for individuals, such as the duration of the quarantine, or the information provided by public authorities brooks2020. In this study, we therefor focus on those factors that are within the control of individuals. However, investigating such factors independently might make little sense since they are interlinked. For example, studying the relations between anxiety and stress with well-being in isolation is less informative, as both anxiety and stress are negatively associated with well-being de2014emotion; Spitzer2006GAD7. However, knowing which of the two has a more substantial impact on people’s well-being above and beyond the other is crucial, as it allows inter alia policymakers, employers, and mental health support organizations to provide more targeted information, create programs that are aimed to reduce people’s anxiety or stress levels, and improve people’s well-being, since anxiety and stress are conceptually independent constructs. Thus, it is essential to study these variables together rather than separately.

2.2 Productivity in Remote Work

The containment measures not only come at a cost for people’s well-being but they also negatively impact their productivity. For example, the International Monetary Fund (IMF) estimated in June 2020 that the World GDP would drop by 4.9% as a result of the containment measures taken to reduce the spread of COVID-19 – with countries particularly hit by the virus, such as Italy, would experience a drop of over 12% imf2020world. This expected drop in GDP would be significantly larger if many people were unable to work remotely from home. However, previous research on the impact of quarantine typically focused on people’s mental and physiological health, thus providing little evidence on the effect on productivity of those who are still working. Luckily, the literature on remote work, also known as telework, allows us to get a broad understanding of the factors that improve and hinder people’s productivity during quarantine.

The number of people working remotely has been growing in most countries already before the COVID-19 pandemic owl_labs_2019; gallup2020. Of those working remotely, 57% do so for all of their working time. The vast majority of remote workers, 97% would recommend others to do the same buffer2020, suggesting that the advantages of remote work outweigh the disadvantages. The majority of people who work remotely do so from the location of their home buffer2020.

Working remotely has been associated with a better work-life balance, increased creativity, positive affect, higher productivity, reduced stress, and fewer carbon emissions because remote workers commute less owl_labs_2019; buffer2020; anderson2015impact; bloom2015does; vega2015within; baruch2000teleworking; cascio2000managing. However, working remotely also comes with its challenges. For example, challenges faced by remote workers include collaboration and communication (named by 20% of 3,500 surveyed remote workers), loneliness (20%), not being able to unplug after work (18%), distractions at home (12%), and staying motivated (7%) buffer2020. While these findings are informative, it is unclear whether they can be generalized. For instance, if mainly those with a long commute or those who feel comfortable working from home might prefer to work remotely, it would not be possible to generalize to the general working population.

A pandemic such as the one caused by COVID-19 in 2020 forces many people to work remotely from home. Being in a frameless and previously unknown work situation without preparation intensifies common difficulties in remote work. Adapting to the new environment itself and dealing with additional challenges adds on to the difficulties already previously identified and experienced by remote workers, and could intensify an individual’s stress and anxiety and negatively affect their working ability. The advantages of remote work might, therefore, be reduced or even omitted. Substantial research is needed to understand further what enables people to work effectively from home while being quarantined kotera2020psychological. The current situation shows how important research in this field is already. Forecasts indicate that remote work will grow on an even larger scale than it did over the past years owl_labs_2019; gallup2020, therefore research results on predictors of productivity while working remotely will increase in importance. Some guidelines have been developed to improve people’s productivity, such as the guidelines proposed by the Chartered Institute of Personnel and Development, an association of human resource management experts CIPD. Examples include designating a specific work area, wearing working clothes, asking for support when needed, and taking breaks. However, while potentially intuitive, empirical support for those particular recommendations is still missing.

Adding to the complexity, the measurement of productivity, especially in software engineering, is a debated issue, with some authors suggesting not to consider it at all Ko2019. Nevertheless, individual developer’s productivity has a long investigation tradition sackman1968exploratory. Prior work on developer productivity primarily focused on developing software tools to improve professionals’ productivity kersten2006using or identifying the most relevant predictors, such as task-specific measurements and years of experience dieste2017empirical. Similarly, understanding relevant skillsets of developers that are relevant for productivity has also been a typical line of research li2015makes. Eventually, as La Toza et al. pointed out, measuring productivity in software engineering is not just about using tools; instead, it is about how they are used and what is measured latoza2020explicit.

3 Research Design

In the present research, we build on the literature discussed above to identify predictors of well-being and productivity. Additionally, we also include variables that were identified as relevant by other lines of research. Furthermore, we chose a different setting, sampling strategy, and research design than most of the prior literature. This is important for several reasons. First, many previous studies included only one or a few variables, thus masking whether other variables primarily drive the identified effects. For example, while boredom is negatively associated with well-being farmer1986boredom, it might be that this effect is mainly driven by loneliness, as lonely people report higher levels of boredom farmer1986boredom – or vice versa. Only by including a range of relevant variables, it is possible to identify the primary variables, which can subsequently be used to write or update guidelines to maintain one’s well-being and productivity while working from home. Second, this approach simultaneously allows us to test whether models developed in an organizational context such as the two-factor theory herzberg2017motivation can also predict people’s well-being in general and whether variables that were associated with well-being for people being quarantined also explain productivity.

Third, while previous research on the (psychological) impact of being quarantined brooks2020 is relevant, it is unclear whether this research is generalizable and applicable to the COVID-19 pandemic. In contrast to previous pandemics, during which only some people were quarantined or isolated, the COVID-19 pandemic strongly impacted billions globally. For example, previous research found that people who were quarantined were stigmatized, shunned, and rejected lee2005experience; this is unlikely to repeat as the majority of people are now quarantined. Fourth, research suggests karesh2012ecology that pandemics become increasingly likely due to a range of factors (e.g., climate change, human population growth) which make it more likely that pathogens such as viruses are transmitted to humans. This implies that it would be beneficial to prepare ourselves for future pandemics that involve lockdowns. Fifth, the trend to remote work has been accelerated through the COVID-19 pandemic Meister2020, which makes it timely to investigate which factors predict well-being and productivity while working from home. The possibility to study this under extreme conditions (i.e., during quarantine), is especially interesting as it allows us to include more potential stressors and distractors of productivity. This is critical. As outlined above, previous research on the advantages and challenges of remote work can presumably not be generalized to the population because mainly people from certain professions and specific living and working conditions might have chosen to work remotely. Sixth and finally, a longitudinal design allowed us to test for causal inferences. Specifically, in wave 1, we identified variables that explain unique variance in well-being and productivity, which we measured again in waves 2. This is important because it is possible that, for example, the amount of physical activity predicts well-being or that well-being predicts physical activity. Additionally, we are able to test whether well-being predicts productivity or vice versa – previous research found that they are interrelated krekel2019employee; carolan2017improving.

The variables we are planning to measure in the present longitudinal study are displayed in Figure 1. To facilitate its interpretation, we categorized the variables in four broad sets of predictors, which are partly overlapping. We include all variables related to people’s well-being and productivity that we discussed above and measured on an individual level. To summarize, while the initial selection of predictors is theory-driven, based on previous research, or recent guidelines, the selection of predictors included in the second wave is data-driven.

Figure 1: Overview of the independent and dependent variables

During the COVID-19 pandemic, many governments and organizations have called for volunteers to support self-isolation (see, for example,  nhs_2020support; nyc_2020). While also relevant to the community at large, research suggests that acts of kindness have a positive effect on people’s well-being buchanan2010acts. Additionally, volunteering has the benefit of leaving one’s home for a legitimate reason and reducing cabin fever. We therefore decided to include volunteering as a potential predictor for well-being.

Coping strategies such as making plans or reappraising the situation are, in general, effective for one’s well-being webb2012dealing; carver1989assessing. For example, altruistic acceptance – accepting restrictions because it is serving a greater good – while being quarantined was negatively associated with depression rates three years later liu2012depression. Conversely, believing that the quarantine measures are redundant because COVID-19 is nothing but ordinary flu or was intentionally released by the Chinese government (i.e., beliefs in conspiracy theories), will likely lead to dissatisfaction because of greater feelings of non-autonomy. Indeed, beliefs in conspiracy theories are associated with lower well-being freeman2017concomitants.

We further propose that three needs are relevant to people’s well-being and productivity cacioppo_need_1982; ryan2000self. Specifically, we propose that the need for autonomy and competence are deprived of many people who are quarantined, which negatively affects well-being and motivation calvo2020health. Further, we propose that the need for competence was deprived, especially for those people who cannot maintain their productivity-level. This might especially be the case for those living with their families. In contrast, the need for relatedness might be over satisfied for those living with their family.

Another important factor associated with one’s well-being is the quality of one’s social relationships birditt2007relationship. As people have fewer opportunities to engage with others they know less well, such as colleagues in the office or their sports teammates, the quality of existing relationships becomes more important, as having more good friends facilitates social interactions either in person (e.g., with their partner in the same household) or online (e.g., video chats with friends).

Moreover, we expect that extraversion is linked to well-being and productivity. For example, extraverted people prefer more sensory input than introverted people ludvigh1974extraversion, which is why they might struggle more with being quarantined. Extraversion correlated negatively with support for social distancing measures carvalho2020personality, which is a proxy of stimulation (e.g., being closer to other people, will more likely result in sensory stimulation). Finally, research on predictors of productivity while working from home can be theoretically grounded in models of job satisfaction and productivity, such as Herzberg’s two-factor theory herzberg2017motivation. This theory states that causes of job satisfaction can be clustered in motivators and hygiene factors. Motivators are intrinsic and include advancement, recognition, work itself, growth, and responsibilities. Hygiene factors are extrinsic and include the relationship with peers and supervisor, supervision, policy and administration, salary, working conditions, status, personal link, and job security. Both factors are positively associated with productivity bassett2005does. As there are little differences between remote and on-site workers in terms of motivators and hygiene factors green2009exploring, the two-factor theory provides a good theoretical predictor of productivity of people working remotely.

3.1 Participants

In our two-wave study, we are covering an extensive set of 51 predictors, as identified above. Based on the literature mentioned earlier, we expected the strength of the association between the predictors and the outcomes’ well-being and productivity to vary between medium to large. Therefore, we assumed for our power analysis a medium-to-large effect size of and a power of .80. Power analysis with G*Power 3.1.9.4 faul2009statistical revealed that we would need a sample size of 190 participants.

To ensure data quality and consistency, and to account for potential dropout in participants between the two waves, we invited almost 500 participants who were identified as software engineers in a previous study russo2020gender to participate in a screening study in April 2020.

To collect our responses, we used Prolific,222www.prolific.co a data collection platform, commonly used in Computer Science (see e.g.Hosio2020CrowdsourcingDiets). We opted for this solution because of the high reliability, replicability, and data quality of dedicated platforms, especially compared with the use of mailing lists peer2017beyond; palan2018prolific. To administer the surveys, we used Qualtrics333www.qualtrics.com and shared it on the Prolific platform.

The screening study was tailored for the COVID-19 pandemic and was completed by 305 professionals. Here, we aimed to select only participants from countries where lockdown measures where put into place. Countries with unclear, mixed policies or early reopening (e.g., Denmark, Germany, Sweden) were excluded. Similarly, our participants were supposed to actively work from home during the lockdown for more than 20h a week.

In the first wave of data collection, which took place in the week of April 20–26 2020, 192 participants completed the first survey. Participation in the second wave (May 4–10) was high (96%), with 184 completed surveys. Participants have been uniquely identified through their Prolific ID, which was essential to run the longitudinal analysis while allowing participants to remain anonymous.

In each survey, we included three test items (e.g., “Please select response option ‘slightly disagree”’). Moreover, we controlled if the participants were still working from home in the reference week and if lockdown measures were still in place in their respective countries. As none of our participants failed at least two of the three test items, all participants reported working remotely and answered the survey in an appropriate time frame, and we did not exclude anyone.

The mean age of the 192 participants was 36.65 years ( = 10.77, range = 19–63; 154 women, 38 men). Participants were compensated in line with the current US minimum wage (average completion time 1202 seconds,  = 795.41).

3.2 Longitudinal design

We employed a longitudinal design, with two waves set two-weeks apart from each other towards the end of the lockdown, which allowed us to test for internal replication. Also, running this study towards the end of the lockdowns in the vast majority of countries allowed participants to provide a more reliable interpretation of lockdown conditions. We chose a period of two weeks because we wanted to balance change in our variables over time with the end of a stricter lockdown that was discussed across many countries when we run wave 2. Many of our variables are thought to be stable over time. That is, a person’s scores on X at time 1 is strongly predictive of a person’s scores on X at time 2 (indeed, the test-retest reliabilities we found support this assumption, see Table 1). The closer the temporal distance between wave 1 and 2, the higher the stability of a variable. In other words, if we had measured the same variables again after only one or two days, there would not have been much variance that could have been explained by any other variable, because X measured at time 1 already explains almost all variance of X measured at time 2. In contrast, we aimed to collect data for wave 2 while people were still quarantined. If at time 1 of the data collection people would still be in lockdown and at time 2 the lockdown would have been eased, this would have included a major confounding factor. Thus, to balance those two conflicting design requirements, we opted for a two weeks break in between the two waves.

We describe the measures of the two dependent (or outcome) variables in Subsection 3.3. Predictors (or independent variables) are explained in Subsections 3.4, 3.5, 3.6, and 3.7. Wherever possible, we relied on validated scales. If this was not possible (e.g., COVID-19 specific conspiracy beliefs), we created a scale. All items are listed in the Supplemental Materials. Additionally, we also explore whether there are any mean changes in the variables we measured at both times (e.g., has people’s well-being changed?)

3.3 Measurement of the dependent variables

Well-being was measured with an adapted version of the 5-item Satisfaction with Life Scale diener1985satisfaction. We adapted the items to measure satisfaction with life in the past week. Example items include “The conditions of my life in the past week were excellent” and “I was satisfied with my life in the past week”. Responses were given on a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree, , ).

Productivity was measured relative to the expected productivity. We contrasted productivity in the past week with the participant’s expected productivity (i.e., productivity level without the lockdown). As we recruited participants working in different positions, including freelancers, we can neither use objective measures of productivity nor supervisor assessments and rely on self-reports. We expect limited effects of socially desirable responses as the survey was anonymous. The general understanding and the widespread belief that many people could not be as productive as they usually are during the lockdown in 2020 (e.g., due to stress or caring responsibilities). We operationalized productivity as a function of time spent working and efficiency per hour, compared to a normal week. Specifically, we asked participants: “How many hours have you been working approximately in the past week?” (Item P1), “How many hours were you expecting to work over the past week assuming there would be no global pandemic and lockdown?” (Item P2)To measure perceived efficiency, “If you rate your productivity (i.e., outcome) per hour, has it been more or less over the past week compared to a normal week?” (Item P3). Responses to the last item were given on a bipolar slider measure ranging from ‘ less productive’ to ‘0%: as productive as normal’ to ‘ more productive’ (coded as -100, 0, and 100). To compute an overall score of productivity for each participant, we used the following formula: productivity = (P1/P2) ((P3 + 100)/100). Values between 0 and .99 would reflect that people were less productive than normal, and values above 1 would indicate that they were more productive than usual. For example, if one person worked only 50% of their normal time in the past week but would be twice as efficient, the total productivity was considered the same compared to a normal week. We preferred this approach over the use of other self-report instruments, such as the WHO’s Health at Work Performance Questionnaire kessler2003world, because we were interested in the change of productivity while being quarantined as compared to ‘normal’ conditions. The WHO’s questionnaire, for example, assesses productivity also in comparison to other workers. We deemed this unfit for our purpose as it is unclear to what extent software engineers who work remotely are aware of other workers’ productivity. Also, our measure consists of only three items and showed good test-retest reliability (Table 1). Test-retest reliability is the agreement or stability of a measure across two or more time-points. A coefficient of 0 would indicate that responses at time 1 would not be linearly associated with those at time 2, which is typically undesired. Higher coefficients are an additional indicator of the reliability of the measures, although they can be influenced by a range of factors such as the internal consistency of the measure itself and external factors. For example, the test-rest reliability for productivity is lower than for most other variables such as needs or well-being, but this is because the latter constructs are operationalized as stable over time. In contrast, productivity can vary more extensively due to external factors such as the number of projects or the reliability of one’s internet connection.

3.4 Psychological factors

Self-discipline was measured with 3-items of the Brief Self-Control Scale tangney2004high. Example items include “I am good at resisting temptation” and “I wish I had more self-discipline” (recoded). Responses were registered on a 5-point scale ranging from 1 (Not at all) to 5 (Very; ).

Coping strategies was measured using the 28-item Brief COPE scale, which measures 14 coping dimensions Carver1997BriefCOPE. Example items include “I’ve been trying to come up with a strategy about what to do” (Planning) and “I’ve been making fun of the situation” (Humor). Responses were on a 5-point scale ranging from 0 (I have not been doing this at all) to 4 (I have been doing this a lot). The internal consistencies were satisfactory to very good for two-item scales: Self-distraction (), active coping (), Denial (), Substance use (), Use of emotional support (), Use of instrumental support (), Behavioral disengagement (, ), Venting (), Positive reframing (), Planning (), Humor (), Acceptance (), Religion (), and Self-blame (, ).

Loneliness was measured using the 6-item version of the De Jong Gierveld Loneliness Scale gierveld2006. The items are equally distributed among two factors, emotional; , ) (e.g., “I often feel rejected”) and social; , (e.g., “There are plenty of people I can rely on when I have problems”). Participants indicated how lonely they felt during the past week. Responses were given on a 5-point scale ranging from 1 (Not at all) to 5 (Every day).

Compliance with official recommendations was measured using three items of a compliance scale wolf2020importance. The items are ‘Washing hands thoroughly with soap’, ‘Staying at home (except for groceries and 1x exercise per day)’ and ‘Keeping a 2m (6 feet) distance to others when outside.’ Reponses were given on a 7-point scale ranging from 1 (never complying to this guideline) to 7 (always complying to this guideline, ).

Anxiety was measured using an adapted version of the 7-item Generalized Anxiety Disorder scale Spitzer2006GAD7. Participants indicate how often they have experienced anxiety over the past week to different situations. Example questions are “Feeling nervous, anxious, or on edge” and “Not being able to stop or control worrying”. Responses were given on a 5-point scale ranging from 1 (Not at all) to 5 (Every day, , ). Additionally, we measured specific COVID-19 and future pandemic related concerns with two items “How concerned do you feel about COVID-19?” and “How concerned to you about future pandemics?” Responses on this were given by a 5-point scale ranging from 1 (Not at all concerned) to 5 (Extremely concerned; Nelson2020psychological.

Stress was measured using a four-item version of the Perceived Stress Scale Cohen1988perceived. Participants indicate how often they experienced stressful situations in the past week. Example items include “In the last month how often have you felt you were unable to control the important things in your life?” and “In the last month how often have you felt confident about your ability to handle your personal problems?”. Responses were registered on a 4-point scale ranging from 1 (Never) to 4 (Very often; , ).

Boredom was measured using the 8-item version struk2017short of the Boredom Proneness Scale farmer1986boredom. Example items include “It is easy for me to concentrate on my activities” and “Many things I have to do are repetitive and monotonous”. Responses were on a 4-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree; , ).

Daily Routines was measured with five items: “I am planning a daily schedule and follow it”, “I follow certain tasks regularly (such as meditating, going for walks, working in timeslots, etc.)”, “I am getting up and going to bed roughly at the same time every day during the past week”, “I am exercising roughly at the same time (e.g., going for a walk every day at noon)”, and “I am eating roughly at the same time every day”. Responses were taken on a 7-point Likert scale ranging from 1 (Does not apply at all) to 7 (Fully applies; , ).

Conspiracy beliefs was measured with a 5-item scale as designed by ourselves for this study. The first two items were adapted from the Flexible Inventory of Conspiracy Suspicions wood2017conspiracy, whereas the latter three are based on more specific conspiracy beliefs: “The real truth about Coronavirus is being kept from the public.”, “The facts about Coronavirus simply do not match what we have been told by ‘experts’ and the mainstream media”, “Coronavirus is a bio-weapon designed by the Chinese government because they are benefiting from the pandemic most”, “Coronavirus is a bio-weapon designed by environmental activists because the environment is benefiting from the virus most”, and “Coronavirus is just like a normal flu”. Responses were collected on a 7-point Likert scale ranging from 1 (Totally disagree) to 7 (Totally agree, ).

Extraversion was measured using the 4-item extraversion subscale of the Brief HEXACO Inventory DeVries2013HEXACO. Responses were given on a 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree; , ). Low scores on extraversion are an indication of introversion. Since we found at wave 1 that extraversion and well-being were positively correlated contrary to our hypothesis (see below), and, in our view, contrary to widespread expectations, we decided to measure in wave 2 what participants’ views are regarding the association between extraversion and well-being. We measured expectations with one item: “Who do you think struggles more with the current pandemic, introverts or extraverts?” Response options were ‘Introverts’, ‘Both around the same’, and ‘extraverts’.

Autonomy, competence, and relatedness needs of the self-determination theory ryan2000self was measured using the 18-item balanced measure of psychological needs scale sheldon2012balanced. Example items include “I was free to do things my own way’ (need for autonomy; , ), “I did well even at the hard things” (competence; , ), and “I felt unappreciated by one or more important people” (recoded; relatedness; , ). Participants were asked to report how true each statement was for them in the past week. Responses were given on a 5-point scale ranging from 1 (no agreement) to 5 (much agreement).

Extrinsic and intrinsic work motivation was measured with the 6-item extrinsic regulation 3-item and intrinsic motivation subscales of the Multidimensional Work Motivation Scale gagne2015multidimensional. The extrinsic regulation subscale measures social and material regulations. Specifically, participants were asked to answer some questions about why they put effort into their current job. Example items include “To get others’ approval (e.g., supervisor, colleagues, family, clients …)” (social extrinsic regulation; ), “Because others will reward me financially only if I put enough effort in my job (e.g., employer, supervisor…)” (material extrinsic regulation; ) and “Because I have fun doing my job” (intrinsic motivation; ). Responses were given on a 7-point scale ranging from 1 (not at all) to 7 (completely).

Mental exercise was measured with two items: “I did a lot to keep my brain active” and “I performed mental exercises (e.g., Sudokus, riddles, crosswords)”. Participants indicated the extent to which the items were true for them in the past week on a 7-point scale ranging from 1 (Not at all) to 7 (Very; ).

Technical skills was measured with one item: “How well do your technological skills equip you for working remotely from home?” Responses were given on a 7-point scale ranging from 1 (Far too little) to 7 (Perfectly).

3.5 Physiological factors

Diet was measured with two items ESS2014round7: “How often do you eat fruit, excluding drinking juice?” and “How often do you eat vegetables or salad, excluding potatoes?”. Responses were given on a 7-point scale ranging from 1 (Never) to 7 (Three times or more a day; )

Quality of sleep was measured with one item: “How has the quality of your sleep overall been in the past week?” Responses were given on a 7-point scale ranging from 1 (very low) to 7 (perfectly).

Physical activity was measured with an adapted version of the 3-item Leisure Time Exercise Questionnaire godin1985simple. Participants were be asked to report how many hours in the past they have been mildly, moderately, and strenuously exercising. The overall score was computed as followed godin1985simple: mild + moderate + strenuously. Missing responses for one or more of the exercise types were be treated as 0.

3.6 Social factors

Quality and quantity of social contacts outside of work were measured with three items. We adapted two items from the social relationship quality scale birditt2007relationship and added one item to measure the quantity: “I feel that the people with whom I have been in contact over the past week support me”, “I feel that the people with whom I have been in contact over the past week believe in me”, and “I am happy with the amount of social contact I had in the past week.” Responses were given on a 6-point Likert scale ranging from 1 (Strongly disagree) to 6 (Strongly agree; , ).

Volunteering was measured with three items that measure people’s behavior over the past week: “I have been volunteering in my community (e.g., supported elderly or other people in high-risk groups)”, “I have been supporting my family (e.g., homeschooling my children)” and “I have been supporting friends, and family members (e.g., listened to the worries of my friends)”. Responses were given on a 7-point scale ranging from 1 (Not at all) to 7 (Very often; ).

Quality and quantity of communication with colleagues and line managers was measured with three items: “I feel that my colleagues and line manager have been supporting me over the past week”, “I feel that my colleagues and line manager believed in me over the past week”, and “Overall, I am happy with the interactions with my colleagues and line managers over the past week.” Responses were given on a 6-point Likert scale ranging from 1 (Strongly disagree) to 6 (Strongly agree; , ).

3.7 Situational factors and demographics

Distractions at home was measured with two items: “I am often distracted from my work (e.g., noisy neighbors, children who need my attention)” and “I am able to focus on my work for longer time periods” (recoded). Responses were given on a 5-point scale ranging from 1 (Not at all) to 5 (Very often; , ).

The participants’ living situation was reported in the following categories. Living with (Babies/Infants), (Toddlers), (Children), (Teenager), and (Adults), and additionally, it was displayed with how many people the participant is currently living.

Financial security was measured with two items that reflect the current but also the expected financial situation glei2019growing: “Using a scale from 0 to 10 where 0 means ‘the worst possible financial situation’ and 10 means ‘the best possible financial situation’, how would you rate your financial situation these days?” and “Looking ahead six months into the future, what do you expect your financial situation will be like at that time?”. Responses were given on a 11-point scale ranging from 0 (the worst possible financial situation) to 10 (the best possible financial situation; ).

Office set-up was measured with three items: “In my home office, I do have the technical equipment to do the work I need to do (e.g., appropriate PC, printer, stable and fast internet connection)”, “On the computer or laptop I use while working from home I do have the software and access rights I need”, and ‘My office chair and desk are comfortable and designed to prevent back pain or other related issues”. Responses were given on a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree; ).

Demographic information were assessed with the following items: “What is your gender?”, “How old are you?” “What type of organization do you work in” (public, private, unsure, other), “What is your yearly gross income?” (US20-40,000, US60,001-80,000, US100,000; converted to the participant’s local currency), “In which country are you based?”, “Have you been working from home or remotely in general before February 2020?” (Yes, No, Unsure), “What percentage of your time have you been working remotely (i.e., not physically in your office) over the past 12 months?”, “In which region/state and country are you living?”, “Is there still a lockdown where you are living?”.

4 Analysis

The data analysis consists of two parts. First, we used the data from time 1 to identify the variables that explain variance in participant well-being and productivity beyond the other variables. Second, we used the Pearson product-moment correlation coefficient (

), to identify which variables were correlated with at least  = .30 with well-being and productivity, to test whether they predict our two outcomes over time. is an effect size which expresses the strength of the linear relation between two variables. We used .30 as a threshold as we are interested in identifying variables that are correlated with at least a medium-sized magnitude cohen1992power with one or both of our outcome variables. Also, a correlation of .30 indicates that the effect is among the top 25% in individual difference research gignac2016effect

. Finally, selecting an effect size of this magnitude provides an effective type-I error control, as in total, we performed 103 correlation tests at time 1 alone (51 independent variables correlated with the two dependent variables, which were also correlated among each other). Given a sample size of 192, this effectively changes our alpha level to .0001, which is conservative. This means that it is very unlikely that we erroneously find an effect in our sample even though there is no effect in the population (

i.e., commit the type-I or false-positive error)

We did not transform the data for any analysis. Unless otherwise indicated above, scales were formed by averaging the items. The collected dataset is publicly available to support other researchers in understanding the impact of enforced work-from-home policies.

4.1 Analysis of time 1 data

To test which of the variables listed in Figure 1 explains unique variance in well-being and productivity, we performed two multiple regression analyses with all variables that were correlated with the two outcome variables with

. In the first analysis, well-being is the dependent variable; in the second analysis, we use productivity as the dependent variable. This allows us to identify the variables that explain unique variance in the two dependent variables. However, one potential issue of including many partly correlated predictors is multicollinearity, which can lead to skewed results. If the Variance Inflation Factor (VIF) is larger than 10, multicollinearity is an issue 

Chatterjee1991regression

. Therefore, we tested whether the variance inflation factor would exceed 10 before performing any multiple regression analysis.

4.2 Analysis of longitudinal data

To analyze the data from both time-points, we performed a series of structural equation modeling analyses with one predictor variable and one outcome variable using the R-package lavaan rosseel2012lavaan. Unlike many other types of analyses, structural equation modeling adjusts for reliability westfall2016statistically. Specifically, models were designed with one predictor (e.g., stress), and one outcome (e.g., well-being) both as measured at time 1 and at time 2. We allowed autocorrelations (e.g., between well-being at time 1 and at time 2) and cross-paths (e.g., between stress at time 1 and well-being at time 2). Autocorrelations are essential because without them we might erroneously conclude that, for example, stress at time 1 predicts well-being at time 2 although it is the part of stress which overlaps with well-being, which predicts well-being at time 2 rogosa1980critique. To put it simply, we can only conclude that X1 predicts Y2 if we control for Y1. No items or errors were allowed to correlate. This is usually done to improve the model fit but has also been criticized as atheoretical: To determine which items and errors should be allowed to correlate to improve model fit can only be done after the initial model is computed and thus a data-driven approach which emphasizes too much on the model fit gana2019structural. The regression (or path) coefficients and associated -values were not affected by the type of estimator. We compared in our analyses the standard maximum likelihood (ML), the robust maximum likelihood (MLR), and the multi-level (MLM) estimator.

5 Results

5.1 Correlations

The pattern of correlations was overall consistent with the literature. At time 1, 16 variables were correlated with well-being at (Table 1)444The Pearson’s correlation coefficient () represents the strength of a linear association between two variables and can range between -1 (perfect negative linear association), 0 (no linear association), to 1 (perfect positive linear association). The regression coefficient B indicates how much the outcome changes if the predictor increases by one unit. For example, the B of stress predicting well-being is -.60. This indicates that a person who has a well-being level of 5 has a stress level that is of -.60 units lower than a person who has a well-being level of 6.. Stress, quality of social contacts, , and need for autonomy, were strongest associated with well-being (all .0001). The pattern of results from the 14 coping strategies were also in line with the literature carver1989assessing: self-blame, , behavioral disengagement, , and venting were negatively correlated with well-being. Interestingly, generalized anxiety was more strongly associated with well-being than COVID-19 related anxiety ( vs ) which might suggest that specific worries have a less negative impact on well-being555A multiple regression with generalized anxiety and COVID-19 related anxiety supports this interpretation: Only generalized anxiety, , but not COVID-19 related anxiety, . This suggests that whether people are worried about COVID-19 specifically has little impact on their well-being. Their general level of anxiety matters substantially, however.. Contrary to our expectations, extraversion was positively correlated with well-being, both at waves 1 and 2. The pattern of the associations was similar at time 2. A reason for participants’ misinterpretation of the intensity to struggle with working from home for introverts could be explained by introverts usually having to avoid unwanted social interactions, and due to being quarantined, they now have to put effort into having social interactions actively. The added challenge to contribute more energy than usual to not being too lonely and changing their usual behavioral pattern demands much more from introverts than extraverts.
At time 1, four variables were correlated with productivity at (Table 1): Need for competence, , distractions, , boredom, , and communication with colleagues and line-managers . Surprisingly, work motivations were uncorrelated with well-being at . At time 2, only distraction was still correlated with productivity, . The strength of association of most variables with productivity dropped between time 1 and 2, which means that those variables associated with productivity at wave 1 were no longer or less strongly associated with productivity at wave 2. The strengths of correlations remained the same when we computed Spearman’s rank correlation coefficients rather than Pearson’s correlations (Spearman’s coefficient is a non-parametric version of Pearson’s and ranges also between -1 and 1).

5.1.1 Additional analysis regarding extraversion

At time 2, we added additional questions to better understand the counter-intuitive finding that well-being and extraversion are positively correlated.

Interestingly, the finding that extraversion is positively correlated with well-being during lockdown is contrary to the expectations of most participants. When asked whether introverts or extraverts struggle more with the COVID-19 pandemic, only 2 participants correctly predicted introverts, where 136 stated extraverts, with 46 participants believing that both groups struggle equally. This highlights the value of our research because people’s intuition can be blatantly wrong.

Through an analysis of the participants’ statements about the informant’s (I) choice, the explanation became more articulated. We now report selected quotes from participants, including their level of extraversion, in wave 1666Scores close to 1 are indicative of an introverted personality trait, while 5 of an extraverted one.. Some informants reported their direct experience supporting the feeling that extraverts struggle more than introverts.

I’m introverted, and I don’t feel the pandemic has affected me at all. Rules aren’t hard to follow and haven’t feel bad. I feel for extraverts; they would struggle a bit with the rules.” [I-101, extraversion score=2.75]

I’m an extravert; my wife is an introvert. I’m really struggling. She’s fine.” [I-92, extraversion score = 5.00]

Nonetheless, a minority of participants also provide alternative interpretations. According to those, both introverts and extraverts have difficulties in reaching out to people, although in different ways. The motivation for such answers is that both personality types struggle with different challenges.

Both types need company, just that each needs company on their own terms. Introverts prefer deeper contact with fewer people and extraverts less deep contact with a greater number of people.” [I-80, extraversion score = 3.75]

extraverts miss human contact; introverts find it even harder to mark their presence online (e.g., in meetings).” [I-160, extraversion score = 3.50]

Interestingly, there is one informant which provide an insightful interpretation, aligned with our results.

Introverts usually have more difficulty communicating with others, and confinement worsens the situation because they will not try to talk to others through video conferences.” [I-136, extraversion score = 2.75]

The lack of a structured working setting, where introvert are routinely involved, causes further isolation. Being ‘forced’ to work remotely significantly increased difficulty in engaging with social contacts. This means that introverts have to put much more effort into interacting with others instead of their typical behavior of reduced interaction in office-based environments. Whereas extraverts have it easier to find some way to maintain their social contacts, introverts might struggle more. Thus, the lockdown had a more negative impact on the well-being of introverts than of extraverts, as shown in Table 1.

rWB1 BWB1 rPR1 BPR1 rWB2 BWB2 rPR2 BPR2 rit
Well-being (WB) 1.00 .18** 1.00 .20** .72***
Productivity (PR) .18* 1.00 .20** 1.00 .50***
Boredom -.42*** -.05 -.33*** -.05 -.33*** .14 -.15* -.02 .69***
Behavioral-disengagement -.31*** .12 -.15* -.41*** -.03 -.08 .54***
Self-blame -.36*** .01 -.21** -.40*** -.08 -.07 .61***
Relatedness .47*** .03 .22** .48*** -.04 .05 .71***
Competence .41*** -.20 .37*** .09 .38*** -.33* .22** .07 .65***
Autonomy .48*** .20 .17* .54*** .35* .09 .76***
Communication .41*** .07 .30*** .04 .39*** .03 .19** .02 .67***
Stress -.58*** -.60*** -.27*** -.54*** -.34* -.08 .73***
Daily-routines .37*** .12* .25*** .42*** .05 .11 .73***
Distractions -.23** .06 -.34*** -.06 -.33*** .00 -.26*** -.08 .63***
Generalized-anxiety -.46*** .01 -.21** -.53*** -.07 -.09 .76***
Emotional-loneliness -.41*** -.13 -.23** -.45*** -.14 -.16* .72***
Social-loneliness -.37*** .08 -.13 -.47*** -.01 -.08 .69***
Quality of social contacts .49*** .22* .24*** .53*** .30** .12 .66***
Extraversion .32*** .22 .24*** .28*** -.00 .08 .74***
Quality-of-Sleep .42*** .05 .27*** .48*** .14* .14 .76***
Conspiracy -.04 .01
Self-distraction -.12 .06
Active-coping .22** .05
Denial -.12 .00
Substance-use -.08 -.11
Emotional-support .10 -.04
Instrumental-support -.09 -.11
Venting -.28*** -.15*
Positive-reframing .19** -.06
Planning -.09 -.09
Humor .07 -.13
Acceptance .20** .01
Religion -.12 -.18*
Office-setup .14 .10
Self-Control .26*** .17*
Volunteering .07 .01
Diet .17* .16*
Exercising-overall .10 .00
Financial-situation .27*** .19**
Covid19-anxiety -.25*** .13
Mental-exercise .25*** .18*
Extrinsic-social -.10 -.04
Extrinsic-material -.22** -.13
Intrinsic-motivation .26*** .22**
People .03 .09
Technological-Skills .24*** .19**
Time-remote -.06 -.04
Age -.06 .07

Note. r: correlations, B: unstandardized regression estimates, rit: test-rest correlation.
Signif. codes: , , ,

Table 1: Correlations at time 1 and 2, unstandardized regression coefficients , and test-retest reliabilities

5.2 Unique influence – Multiple regression analyses

To test which of the predictors had a unique influence on well-being and productivity, we included all variables that were correlated with either outcome with at least .30 at time 1. This is a conservative test because many predictors are correlated among each other and thus taking variance from each other. Also, it allowed us to repeat the same analysis at time 2 because all predictors which correlated with either well-being or productivity at time 1 with were included at time 2. In a first step, we tested whether multicollinearity was an issue. This was not the case, with VIF 4.1 for all four regression models and thus clearly below the often-used threshold of 10 Chatterjee1991regression.

Sixteen variables correlated with well-being (Table 1). Together, they explained a substantial amount of variance in well-being at time 1, , and at time 2, . At time 1, stress (negatively), social contacts, and daily routines uniquely predicted well-being at (see Table 1, column 3, and Table 3). At time 2, need for competence and autonomy, stress, quality of social contacts, and quality of sleep uniquely predicted well-being at (see Table 1, column 7, and Table 5). Together, stress and quality of social contacts predicted at both time points significantly well-being. Four variables correlated with productivity (Table 1). Together, they explained 16% of variance in productivity at time 1, , and 8% at time 2, . At both time points, none of the four variables explained variance in productivity beyond the other three variables, suggesting that they all are associated with productivity but we lack statistical power to disentangle the effects (Tables 3 and  5).

There is an ostensible discrepancy between some correlations and the estimates of the regression analyses which requires further explanations. An especially large discrepancy appeared for the variable need for competence, which correlated positively with well-being at time 1 and 2,  = .41 with   .001, and  = .38 with   .001, but was negatively associated with well-being when controlling for other variables in both regression analyses,  = -.20 with  = .24, and  = -.33 with  = .04. This suggests that including a range of other variables, that serve as control variables, impact the results. Indeed, exploratory analyses revealed that need for competence was no longer associated with well-being when we included need for autonomy. That is, when we performed a multiple regression with the needs for autonomy and competence as the only predictors, need for competence became non-significant. Need for competence also includes an autonomy competent, which might explain this. It is easier to fulfill one’s need for competence while being at least somewhat autonomous ryan2000self. Further, including generalized anxiety and boredom reversed the sign of the association: Need for competence became negatively associated with well-being. Including those two variables remove the variance that is associated with enthusiasm (boredom reversed) and courage (generalized anxiety reversed), which might explain the shift to negative association with well-being. Together, controlling for need for autonomy, generalized anxiety, and boredom, takes away positive aspects of need for competence, leaving a potentially cold side that might be closely related to materialism, which is negatively associated with well-being dittmar2014relationship.

Estimate Std. Error t value Pr(t)
Boredom -0.047 0.100 -0.474 0.636
Behavioral disengagement 0.120 0.112 1.073 0.285
Self blame 0.013 0.113 0.116 0.908
Relatedness 0.025 0.173 0.147 0.884
Competence -0.201 0.169 -1.186 0.237
Autonomy 0.203 0.171 1.188 0.237
Communication 0.073 0.106 0.690 0.491
Stress -0.605 0.178 -3.393 0.001
Daily routines 0.125 0.061 2.038 0.043
Distractions 0.061 0.105 0.580 0.563
Generalized anxiety 0.010 0.146 0.071 0.944
Emotional loneliness -0.126 0.133 -0.948 0.344
Social loneliness 0.082 0.108 0.761 0.447
Social contacts 0.224 0.106 2.125 0.035
Extraversion 0.223 0.127 1.757 0.081
Quality of Sleep 0.053 0.058 0.918 0.360
Signif. codes: , , ,
Table 3: Predictors of productivity wave 1
Estimate Std. Error t value Pr(t)
Boredom -0.053 0.031 -1.675 0.096
Competence 0.088 0.053 1.650 0.101
Communication 0.043 0.034 1.256 0.211
Distractions -0.065 0.036 -1.795 0.074
Signif. codes: , , ,
Table 2: Predictors of well-being wave 1
Estimate Std. Error t value Pr(t)
Boredom 0.144 0.094 1.529 0.128
Behavioral disengagement -0.035 0.140 -0.249 0.804
Self blame -0.075 0.145 -0.518 0.605
Relatedness -0.036 0.156 -0.228 0.820
Competence -0.329 0.159 -2.068 0.040
Autonomy 0.347 0.146 2.380 0.018
Communication 0.033 0.087 0.382 0.703
Stress -0.337 0.157 -2.153 0.033
Daily routines 0.046 0.064 0.728 0.467
Distractions 0.005 0.108 0.046 0.963
Generalized anxiety -0.073 0.134 -0.549 0.583
Emotional loneliness -0.136 0.126 -1.076 0.283
Social loneliness -0.011 0.126 -0.085 0.932
Social contacts 0.304 0.114 2.676 0.008
Extraversion -0.001 0.114 -0.011 0.991
Quality of Sleep 0.144 0.056 2.576 0.011
Signif. codes: , , ,
Table 5: Predictors of productivity wave 2
Estimate Std. Error t value Pr(t)
Boredom -0.015 0.032 -0.479 0.632
Competence 0.065 0.060 1.089 0.278
Communication 0.021 0.032 0.662 0.509
Distractions -0.077 0.041 -1.874 0.063
Signif. codes: , , ,
Table 4: Predictors of well-being wave 2

5.3 Longitudinal analysis

Test-retest reliabilities were good for all variables, supporting the quality of our data (last column of Table 1

, column 10). In total, we performed 20 structural equation modeling (SEM) analyses to test whether well-being and productivity are predicted by or predict any of the 16 independent variables for well-being, including one model in which we tested whether well-being predicts productivity or vice versa, and four models for productivity. Since the probability of a false positive is very high, due to the high number of models analyzed, we used a conservative error rate of .005. We are using a different threshold for the longitudinal analysis than for the correlation analyses since we did a different number of tests for the latter.

One example of our SEM analyses is presented in Figure 2, where we looked at the predictive-causal relationship between stress and well-being in waves 1 and 2. The boxes represent the items and the circles the variables (e.g., stress). The arrows between the items and the variables represent the loadings, that is how strongly each of the items contributes to the overall variable score (e.g., item 3 of the stress scale contributes least and item 4 most to the overall score at both time points). The circular arrows represent errors. The bidirectional arrows between the variables represent the covariances, which are comparable to correlations. The one-handed arrows show causal impacts over time. The arrows between the same variables (e.g., well-being 1 and well-being 2) show how strongly they impact each other and are comparable to the test-retest correlations. The most critical arrows are those between well-being 1 and stress 2 as well as between stress 1 and well-being 2. They show whether one variable causally predicts the other.

The most relevant values in Figure 2 are presented in Table 6. Columns 2-4 show that stress and well-being were significantly associated at time 1,  = -0.75,  = .13, . This association was mirrored at time 2,  = -0.15,  = .05,  = .001 (columns 5-7). Columns 8-10 show that stress at time 1 did not significantly predict well-being at time 2,  = -0.00,  = .16,  = .99. Columns 8-10 of the second part of Table 6 also show that well-being at time 1 did not predict stress at time 2,  = 0.03,  = .05,  = .55. Columns 2-4 of the second part show the autocorrelation of well-being, that is how strongly well-being at time 1 predicts well-being at time 2,  = 0.71,  = .09, . Autocorrelations can be broadly understood as the unstandardized version of the test-retest correlations (reliability) reported in Table 1. Finally, columns 5-7 of the second part show the autocorrelation of stress, which are also significant  = .99,  = .16, . We conclude that no model revealed any significant associations at . Thus, no variable at time 1 (e.g., stress) is able to explain a significant amount of variance in another variable (e.g., well-being) at time 2. We only found a negative tendency regarding with  = -.154, . Furthermore, Table 6 shows which variable is more likely to have a stronger impact on the other over time. For example, has a , , suggesting that it is much more likely that distraction influence negatively productivity, rather than productivity influencing the level of distraction.

Figure 2: SEM analysis of stress and well-being in wave 1 and 2
IV – DV IV – DV IV DV
Independent variable (IV) - Dependent variable (DV) B SE p B SE p B SE p
Well-being – Productivity 0.127 0.048 0.009 0.062 0.027 0.024 0.001 0.02 0.968
Boredom – Well-being -0.729 0.155 <0.001 0.023 0.064 0.72 0.011 0.075 0.88
Behavioral-disengagement – Well-being -0.484 0.126 <0.001 -0.158 0.059 0.007 -0.013 0.1 0.898
Self-blame – Well-being -0.629 0.147 <0.001 -0.167 0.049 0.001 0.072 0.088 0.416
Distractions – Well-being -0.342 0.117 0.004 -0.107 0.051 0.036 0.015 0.118 0.9
Generalized anxiety – Well-being -0.698 0.137 <0.001 -0.187 0.05 <0.001 -0.02 0.086 0.816
Emotional loneliness – Well-being -0.735 0.143 <0.001 -0.166 0.057 0.004 -0.064 0.104 0.535
Social loneliness – Well-being 0.583 0.131 <0.001 0.116 0.056 0.037 0.105 0.086 0.222
Need for Relatedness – Well-being 0.665 0.124 <0.001 0.111 0.049 0.022 0.119 0.107 0.266
Need for Competence – Well-being 0.499 0.108 <0.001 0.084 0.044 0.055 0.121 0.111 0.274
Need for Autonomy – Well-being 0.566 0.109 <0.001 0.142 0.046 0.002 0.352 0.177 0.047
Social contacts – Well-being 0.816 0.151 <0.001 0.162 0.064 0.011 0.059 0.076 0.441
Communication – Well-being 0.641 0.142 <0.001 0.168 0.067 0.013 0.054 0.082 0.506
Stress – Well-being -0.749 0.127 <0.001 -0.148 0.046 0.001 -0.001 0.164 0.993
Daily-routines – Well-being 0.84 0.195 <0.001 0.112 0.072 0.12 0.1 0.069 0.148
Extraversion – Well-being 0.308 0.09 0.001 -0.001 0.03 0.972 -0.027 0.142 0.851
Boredom – Productivity -0.17 0.04 <0.001 0.013 0.025 0.595 -0.032 0.028 0.259
Competence – Productivity 0.139 0.028 <0.001 0.02 0.018 0.264 0.045 0.044 0.306
Communication – Productivity 0.148 0.037 <0.001 -0.007 0.026 0.77 0.03 0.03 0.315
Distraction – Productivity -0.121 0.03 <0.001 0.022 0.02 0.278 -0.154 0.056 0.006
DV DV IV IV DV IV
Independent variable (IV) - Dependent variable (DV) B SE p B SE p B SE p
Well-being – Productivity 0.529 0.07 <0.001 0.698 0.067 <0.001 0.112 0.179 0.531
Boredom – Well-being 0.711 0.072 <0.001 0.753 0.096 <0.001 -0.076 0.054 0.156
Behavioral-disengagement – Well-being 0.7 0.07 <0.001 0.632 0.101 <0.001 -0.092 0.046 0.046
Self-blame – Well-being 0.724 0.073 <0.001 0.533 0.088 <0.001 -0.041 0.037 0.276
Distractions – Well-being 0.705 0.069 <0.001 0.827 0.133 <0.001 -0.056 0.041 0.17
Generalized anxiety – Well-being 0.711 0.075 <0.001 0.73 0.072 <0.001 -0.1 0.01 0.011
Emotional loneliness – Well-being 0.677 0.075 <0.001 0.976 0.114 <0.001 -0.011 0.053 0.84
Social loneliness – Well-being 0.675 0.069 <0.001 0.744 0.08 <0.001 0.124 0.045 0.006
Need for Relatedness – Well-being 0.667 0.073 <0.001 0.712 0.096 <0.001 0.013 0.042 0.759
Need for Competence – Well-being 0.675 0.07 <0.001 0.602 0.092 <0.001 0.031 0.036 0.39
Need for Autonomy – Well-being 0.612 0.078 <0.001 1.193 0.185 <0.001 -0.061 0.047 0.192
Social contacts – Well-being 0.681 0.072 <0.001 0.659 0.077 <0.001 0.097 0.054 0.071
Communication – Well-being 0.685 0.072 <0.001 0.751 0.089 <0.001 0.057 0.054 0.285
Stress – Well-being 0.709 0.091 <0.001 0.987 0.155 <0.001 0.031 0.052 0.547
Daily-routines – Well-being 0.664 0.07 <0.001 0.866 0.116 <0.001 0.039 0.061 0.523
Extraversion – Well-being 0.706 0.069 <0.001 1.024 0.138 <0.001 0.017 0.025 0.486
Boredom – Productivity 0.498 0.073 <0.001 0.843 0.1 <0.001 0.306 0.178 0.087
Competence – Productivity 0.493 0.077 <0.001 0.677 0.096 <0.001 -0.095 0.129 0.461
Communication – Productivity 0.471 0.074 <0.001 0.76 0.085 <0.001 0.149 0.181 0.412
Distraction – Productivity 0.419 0.078 <0.001 0.915 0.161 <0.001 0.084 0.161 0.602

Note

. B: unstandardized regression estimate, SE: Standard Error,

: p-value. Variable is measured in the first wave, and Variable in the second wave.

Table 6: Structural Equation Modeling analyses

Additionally, we explored whether there are any mean changes between time 1 and 2, separately for all 18 variables. For example, has the well-being increased over time? This would suggest that people adapted further within a relatively short period of two weeks to the threat from COVID-19. Table 7 shows that the arithmetic mean () of well-being has indeed slightly increased between time 1 and 2,  = 4.14 vs  = 4.34. A closer look revealed that 91 participants reported higher well-being at time 2 compared to time 1, 23 reported the same level of well-being, and 70 a lower level of well-being. Further, on average people’s score of behavioral disengagement and quality of social contacts increased, whereas emotional loneliness and the quality of communication with line managers and coworkers decreased.

Time 1 Time 2
M SD M SD t p Cohen’s d Higher Smaller Equal
Well-being 4.14 1.367 4.34 1.289 -2.329 0.021 -0.129 91 70 23
Productivity 0.99 0.419 1.032 0.436 -1.575 0.117 -0.116 87 77 19
Boredom 2.936 1.136 2.927 1.158 -0.33 0.742 -0.019 91 79 14
Behavioral-disengagement 1.805 0.936 2.062 1.03 -3.621 <0.001 -0.256 82 40 62
Self-blame 1.812 0.99 1.88 1.013 -0.962 0.337 -0.062 60 52 72
Need for Relatedness 3.497 0.83 3.559 0.803 -1.13 0.260 -0.063 86 73 25
Need for Competence 3.572 0.735 3.582 0.731 -0.04 0.968 -0.002 82 82 20
Need for Autonomy 3.483 0.688 3.511 0.732 -0.572 0.568 -0.029 88 67 29
Communication 4.534 0.996 4.292 1.185 3.244 0.001 0.199 57 81 38
Stress 2.501 0.807 2.52 0.797 -0.593 0.554 -0.032 81 64 39
Daily routines 4.681 1.561 4.717 1.533 -0.108 0.914 -0.006 71 72 41
Distractions 2.466 0.934 2.443 0.895 0.188 0.851 0.012 58 64 62
Generalized anxiety 2.245 1 2.174 1.01 1.246 0.214 0.064 69 90 25
Emotional loneliness 2.111 0.903 2.007 0.871 2.077 0.039 0.114 54 79 51
Social loneliness 2.641 1.004 2.563 1.017 0.807 0.421 0.047 65 79 40
Quality of social contacts 4.109 1.093 4.312 1.077 -2.612 0.010 -0.159 91 54 39
Extraversion 3.448 0.786 3.457 0.778 -0.195 0.846 -0.009 73 59 52
Quality of Sleep 4.13 1.754 4.174 1.686 0.31 0.757 0.016 54 51 79

Note. t: t

-value of a paired sample t-test; Higher: Absolute number of people who scored higher on a variable at time 2 compared to time 1; Lower: Number of people who scored lower at time 2; Equal: People whose score has not changed over time.

Table 7: Within-subject comparisons to analyze mean changes over time

5.4 Conceptual replication analysis

Our finding that office-setup is not significantly related to well-being and productivity seems to contradict a recent cross-sectional study by Ralph et al. Ralph2020pandemic that investigated how the fear of bioevents, disaster preparedness, and home office ergonomics predict well-being and productivity among software developers. In that study, ergonomics was positively related to both well-being and productivity. To measure ergonomics, the authors created six items concerning distractions, noise, lighting, temperature, chair comfort, and overall ergonomics. The first two items are closely related to our measure of distraction, which was negatively associated with well-being in wave 1 of our sample,  = -.23, and productivity, = -.34. In contrast, the following four items are more closely associated with office-setup in our survey, which was positive but not significantly associated with well-being,  = .14, and productivity,  = .10.

To better understand such inconsistency with our result, we run a replication analysis using Ralph et al.’s data. To test whether ergonomics’ effect is mainly driven by distraction and noise, we combined the first two items into variable ergonomics-distractions (recoded, higher scores indicate less distraction) and the other four items into ergonomics-others. Indeed, ergonomics distractions was more strongly correlated with well-being,  = .25, and productivity,  = .29, than was ergonomics-other, s = .19 and .19, respectively. This suggests that our findings replicate those of Ralph et al. and emphasize the importance of distinguishing between distraction and office set-up.

6 Discussion

6.1 Implications and recommendations

The COVID-19 pandemic and the subsequent lockdown have had a definite impact on software professionals who were primarily forced to work from home. The first significant outcome of this research is that there are many variables that are associated with well-being and productivity. Although we could not determine any causal relationship, the effect sizes for both waves are medium to large for several variables which have mainly shown high stability of the results over time. Also, well-being and productivity were positively associated. In other words, neglecting well-being will likely also negatively impact productivity. Therefore, we agree with Ralph et al.’s Ralph2020pandemic recommendation that pressuring employees to keep the average productivity level without taking care of their well-being will lower productivity. However, we would also like to present an alternative interpretation that having productive employees will strengthen their sense of achievement and improve their well-being.

In the following, we focus on practical recommendations based on the most reliable predictors of well-being and productivity that we identified in our study through our regression analysis: need for autonomy, stress, daily routines, social contacts, need for competence competence, extraversion, and quality of sleep as predictors of well-being, in Table 8. Distractions and boredom related to productivity are discussed in Table 9.

Persistent high-stress levels are related to adverse outcomes in the workplace bazarko2013impact and people’s well-being. To reduce stress, Bazarko et al. bazarko2013impact recommend practicing mindfulness-based stress reduction training and practices that can be performed at home. Participating in such a program can lead to lower levels of stress and a lower risk of work burnout. Grossman et al. recommended other stress reduction methods. grossman2004mindfulness. Moreover, Naik et al. naik2018effect, who found that mindfulness meditation practices, slow breathing exercises, mindful awareness during yoga postures, and mindfulness during stressful situations and social interactions can reduce stress levels. Together, the results of these studies suggest that mindfulness practices, even when performed at home, can reduce stress, which could also improve software engineers’ well-being while being quarantined.

The quality of social contacts as part of the overall quality of life has a significant impact on people’s well-being, as discovered in this study. Therefore, employers should be interested in enabling their employees to spend time with people they value and encourage them to build strong, meaningful relationships within their work environment. Creating a virtual office, (e.g., using an online working environment such as ‘Wurkr’) allows people to work with the impression of sharing a physical workspace online to communicate more comfortably and work together from anywhere. For example, in order to simplify conversations, the Slack plugin ‘Donut’ slack2020 randomly connects employees for coffee breaks with the purpose to get to know each other better by spending some time chatting virtually. Besides, our finding that quality of social contact, but not living alone is associated with well-being, is in line with the literature. Quality of contact with one’s partner and family independently predicted negatively depression, whereas the frequency of these contact did not teo2013social. Together, this suggests that findings from the literature can overall be generalized to people being quarantined.

Organizing the day in a structured way at home, appears to be beneficial for software professionals’ well-being. People tend to overwork when working remotely buffer2020. This could be further magnified during quarantine where usual daily routines are disrupted, and thus working might become the only meaningful activity to do. Therefore, it is essential to develop new daily routines in order to not be completely absorbed by work and to prevent a burnout brooks2020. Therefore, scheduling meetings and designating time specifically for hobbies or spending time with family and friends is helpful while working from home and helps to satisfy employees’ needs for social contacts.

To fulfill people’s need for autonomy, it is necessary to allow employees to act on their values and interests wang_can_2016. While coordinating collaborative workflows and managing projects remotely comes with its challenges buffer2020. For remote workers it is crucial to have flexibility in how they structure, organize, and perform their tasks wang_can_2016. It is therefore helpful to delegate work packages instead of individual tasks. This makes it easier for individuals to work self-directedly and thus to fulfill their need for autonomy.

To fulfill employees’ need for competence, it is necessary to provide them with the opportunity to grow personally and advance their skill set legault2006high. Two of the mainly required and highly demanded skills in remote work environments are communication skills and the ability to use virtual tools, such as presentation tools or collaborative project planning tools buffer2020. Raising awareness for the unique requirements of virtual communication is crucial for a smooth working process. Therefore, working remotely requires specific communication skills, such as mindful listening mcmanus2006transparency or asynchronous communication, which allows people to work more efficiently jarvela2002web. Collaborative tools such as GitHub, Trello, Jira, Google Docs, Klaxoon, Mural, or Slack can simplify work processes and enable interactive workflows. Besides the training and development of employees’ specific virtual skill set, it is also recommended to invest in employees’ personal development within the company. Taking action and offering employees the opportunity to grow will not only evolve their role but also strengthen their loyalty towards the employer and, therefore, employee retention kossivi2016study.

Introverted software professionals seem to be more affected by the lockdown than their more extraverted peers. This finding is counter-intuitive since extraverted people prefer more direct contacts than introverted people ludvigh1974extraversion. Our interpretation of these results is that introverts have a much higher burden to reach out to colleagues than extraverted ones. Also, being introverted does not mean that there is no need for social contacts at all. While in the office they had chances to be involved with colleagues both in a structured or unstructured fashion, at home it is much more difficult as they have to be more proactive to reach out to colleagues in a more formalized setting, such as online collaboration platform (e.g., MS Teams). Therefore, software organizations should regularly organize both formal and informal online meeting occasions, where introvert software engineers feel a lower entry barrier to participate.

Quality of sleep is also a relevant predictor for well-being. Although it might sound obvious, there is a robust association between sleep, well-being, and mindfulness howell2008sleep. In particular, Howell et al. found that mindfulness predicts quality of sleep, and quality of sleep and mindfulness predict well-being.

Distractions at home are a challenging obstacle to overcome while working remotely. Designating a specific work area in the home and communicating non-disturbing times with other household members are easy and quick first steps to minimize distractions at the workplace at home. Another obstacle that distracts remote workers more frequently is cyberslacking, which is understood as spending time on the internet for non-work-related reasons during working hours CD. Cyberslacking and its contribution to distractions at home for remote workers were not included in this study but would be worth exploring in future research.

When people experience, boredom it makes them feel “…unchallenged while they think that the situation and their actions are meaningless” (van2012boredom, p. 181). Especially people who thrive in a social setting at work are in danger of being bored quickly while working in isolation from their homes. The enumerated recommendations above, such as assigning interesting, personally tailored, and challenging work packages, using collaborative tools to hold yourself accountable, and having social interactions while working remotely, also help reduce boredom at work. Ideally, employees are intrinsically motivated and feel fulfilled by what they do. If this is not the case over a more extended period, and the experienced boredom is not a negative side effect of being overwhelmed while being quarantined, it might be reasonable to discuss a new field of action and area of responsibility with the employee.

To conclude, working from home certainly comes with its challenges, of which we have addressed several in this study. However, at least software engineers appear to adapt to the lockdown over time, as people’s well-being increased, and the perceived quality of their social contacts improved. Similar results have also been confirmed by a survey study of 2,595 New Zealanders’ remote workers Walton2020NZadaptation. Walton et al. found that productivity was similar or higher than pre-lockdown, and 89% of professionals would like to continue to work from home, at least one day per month. This study also reveals that the most critical challenges were switching off, collaborating with colleagues, and setting up a home office. On the other hand, working from home led to a drastic saving of time otherwise allocated to daily commuting, a higher degree of flexibility, and increased savings.

Findings Recommendations
Autonomy Significant positive predictor in wave 2 ().

Organizations should trust their software engineers about how to reach agreed goals, leaving them a high degree of freedom about how to schedule the day.

Stress Significant negative predictor in both waves (, ). Practice mindfulness-based stress reduction training such as meditation, yoga, and the Wim Hof breathing method.
Daily routines Significant positive predictor in wave 1 (). Establish new routines, dedicating time to work, individual hobbies, and social contacts.
Social contacts Significant positive predictor in both waves (, ). Support at a company level occasions for informal meetings (e.g., online coffee breaks) during working hours.
Competence Significant positive associations between competence and well-being in both waves. Companies train software engineers to work in a remote setting. Similarly, software engineers should be able to choose which kind of competences and training they think are helpful for their careers.
Extraversion Positive predictor in wave 1 () Organizations and peers should proactively reach out to introverted software engineers by involving them in work or non work-related activities.
Quality of sleep Significant positive predictor in wave 2 () Schedule enough sleeping time per night and practice mindfulness for sleep transition.
Table 8: Summary of key findings & recommendations for Well-Being
Findings Recommendations
Boredom Negative predictor in wave 1 (). Organizations should redesign employees goals by letting them choose tasks as much as possible and diversify activities.
Distractions Negative predictor in both waves (, ) Organizations should support software engineers to set up a dedicate home office. Routines and agreements with family members about working times also help to be more focused.
Table 9: Summary of significant key findings & recommendations for Productivity

6.2 Threats to validity

Limitations are discussed using Gren’s five-facets framework gren2018standards.

Reliability. This study used a two-wave longitudinal study, where over 90% of the initial participants, identified through a multi-stage selection process, also participated in the second wave. Further, the test-retest reliabilities were high, and the internal consistencies (Cronbach’s ) ranged from satisfactory to very good.

Construct validity. We identified 51 variables, which were drawn from the literature, and a suitable measurement instrument measured each. Where possible, we used validated instruments. Otherwise, we developed and reported the instruments used. To measure the construct validity, we also reported the Cronbach’s alpha of all variables across both waves. However, we note that despite a large number of variables in our study, we still might have missed one or more relevant variables, which would have been significantly turned out in our analysis.

Conclusion validity

. To draw our conclusions, we used multiple statistical analyses such as correlations, paired t-tests, multiple linear regressions, and structural equation modeling. To ensure reliable conclusions, we used conservative thresholds to reduce the risk of false-positive results. The threshold depended on the number of comparisons for each test. Additionally, we did not include covariates, nor did we stop the data collection based on the results, or performed any other practice that is associated with increasing the likelihood of finding a positive result and increasing the probability of false-positive results 

simmons2011false. However, we could not make any causal-predictive conclusion since all 20 SEM analyses provided non-significant results, using a threshold of significance that reduces the risk of false-positive findings. Finally, we made both raw data and R analysis code openly available on Zenodo.

Internal validity. This study did not lead to any causal-predictive conclusion, which was the main aim of the present study. We can not say that the analyzed variables influence well-being or productivity or vice versa. We are also aware that this study relies on self-reported values, limiting the study’s validity. Further, we adjusted some measures (i.e., productivity). Participants were not supposed to report their perceived productivity but to make a comparison, which has been computed independently afterward in our analysis. We also underwent an extensive screening process, selecting over 190 software engineers of the initial 483 initial suitable subjects. Typical problems related to longitudinal studies (e.g., attrition of the subjects over a long-term period) do not apply. The dropout rate between the two waves has been low (under 10%). We run this study towards the end of the lockdown of the Covid-19 pandemic in spring 2020. In this way, participants were able to report rooted judgments of their conditions. Waves were set at two weeks distance, which ensured that lockdowns had not been lifted yet during the data collection of wave 2, but was also not close enough so that variability in each of the variables would already be sufficiently high between the two-time points. Since this was a pandemic, the surveyed countries’ lockdown conditions have been similar (due to standardized WHO’s recommendations). However, we did not consider region-specific conditions (e.g., severity of virus spread) and recommendations. Also, lockdown timing differed among countries. To control these potential differences, we asked participants at each of the two waves if lockdown measures were still in place, and if they were still working from home. Since all our participants reported positively to both these conditions, we did not exclude anyone from the study.

External validity. Our sample size has been determined by an a priori power analysis, manageable for longitudinal analyses. However, this study was designed to maximize internal validity, focusing on finding significant effects, rather than working with a representative sample of the software engineering population (with , such as Russo and Stol russo2020gender did, where the research goal focused on the generalizability of results).

7 Conclusion

The COVID-19 pandemic disrupted software engineers in several ways. Abruptly, lockdown and quarantine measures changed the way of working and relating to other people. Software engineers, in line with most knowledge workers, started to work from home with unprecedented challenges. Most notably, our research shows that high-stress levels, the absence of daily routines, and social contacts are some of the variables most related to well-being. Similarly, low productivity is related to boredom and distractions at home.

We base our results on a longitudinal study, which involved 192 software professionals. After identifying 51 relevant variables related to well-being or productivity during a quarantine from literature, we run a correlation study based on the results gathered in our first wave. For the second wave, we selected only the variables correlated with at least a medium effect size with well-being or productivity. Afterward, we run 20 structural equation modeling analyses, testing for causal-predictive relations. We could not find any significant relation, concluding that we do not know if the dependent variables are caused by independent ones or vice versa. Accordingly, we run several multiple regression analysis to identify unique predictors of well-being and productivity, where we found several significant results.

This paper confirms that, on average, software engineers’ well-being increased during the pandemic. Also, there is a correlation between well-being and productivity. Out of 51 factors, nine were reliably associated with well-being and productivity. Correspondingly, based on our findings, we proposed some actionable recommendations which might be useful to deal with potential future pandemics.

Software organizations might start to experimentally ascertain whether adopting these recommendations will increase professionals’ productivity and well-being. Our research findings indicate that granting a higher degree of autonomy to employees might be beneficial, on average. However, while extended autonomy might be perceived positively experienced by those with a high need for autonomy, it might be perceived as stressful for those who prefer structure. It is unlikely that any intervention will have the same effect on all people (since there is a substantial variation for most variables), it is essential to have individual differences in mind when exploring the effects of any interventions. Thus, adopting incremental intervention, based on our findings, where organizations can get feedback from their employees, is the recommended strategy.

Future work will explore several directions. Cross-sectional studies with representative samples will be able to test whether our findings are generalizable and do get a better understanding of underlying mechanisms between the variables. We will also investigate the effectiveness of specific software tools and their effect on the well-being and productivity of software engineering professionals with particular regard to the relevant variables.

Supplementary Materials

The full survey, raw data, and R analysis code are openly available on Zenodo DOI: https://doi.org/10.5281/zenodo.3959131.

Acknowledgment

We thank the Editors-in-Chief for fast-tracking our manuscript. The authors would also like to thank Gabriel Lins de Holanda Coelho for initial feedback on this project.

References