Working from home during the COVID-19 pandemic: Impact on office worker productivity and work experience

2.1Worker characteristics

The WFH experience is inevitably influenced by worker demographics, including gender, age, and income [14]. Even in 2021, gender gaps in the workplace and at home persist [15], which might lead to the assumption that women have lower productivity than men because women spend more time on household chores and child caregiving [16, 17]. However, research prior to the pandemic has shown that women’s productivity is similar –if not higher—compared to men’s productivity [18]. Income and age have also been well-studied relative to work-related productivity, noting that higher-waged, middle-aged workers are more productive than lower-waged, younger counterparts [19, 20].

The impact of these demographic factors on productivity in remote work is less clear. Applying a traditional perspective of women’s engagement in household tasks would suggest potential negative impacts on productivity, while an equity approach that considers increased involvement of men in household duties could level any gender gaps in effects on productivity during remote work [17]. Similarly, other factors at home, such as the presence of family members due to the pandemic, could alter the conventional age-income-productivity association. For example, middle-aged workers who are working alongside their children, could become overwhelmed due to parenting demands at the expense of their work engagement and this can negatively impact their productivity [21].

In addition to the demographic factors, WFH may create different challenges for workers with different occupational backgrounds. Prior studies have investigated the impact of WFH on productivity within specific groups of workers (e.g., workers of a Chinese travel agency [7], workers of U.S. Patent and Trademark Office [22]). However, there has not been a study that investigated effects of WFH on productivity across different occupational groups. A transition to remote work would likely have low risk of loss in efficiency for workers who primarily engage directly with computer workstations throughout their day (e.g., programmers) as opposed to individuals working in jobs that require mixed tasks in an interpersonal environment (e.g., health care office workers). In addition to potential negative impact on efficiency in job performance, some of these occupational groups that do not typically spend their entire day at a computer would likely experience a dramatic shift in the amount of sedentary time at their workstations. For example, WFH during the pandemic has led lawyers and judges to spend more time at their workstations to virtually attend court trials [23].

Increased sedentary work and other potential aspects of personal health and well-being are important considerations in the discussion of WFH. Importantly, worker health has been consistently associated with productivity, such that the more health issues workers report the worst their productivity levels are [24]. A variety of physical health issues such as eye strain, nose related symptoms, fatigue, and headache, as well as mental health issues such as anxiety, depression, stress, and insomnia can all have a negative impact on productivity. Specifically, there is a growing body of evidence related to the concept of worker presenteeism that demonstrates degraded work performance due to the existence of physical and mental health issues [25]. Although it may be more easily concluded that these relationships would exist similarly in remote contexts, there are limited studies that examine the impact of worker health on productivity while WFH. Based on this review, we pose the following research question:

Research question 1: Do workers’ demographics (i.e., gender, age, income, and occupation) and their physical and mental health statuses affect workers’ productivity and the time spent at workstation when work is performed from home during the pandemic period?

2.2Workspace context

Workspace context plays a major role in shaping the work experience. Satisfaction with one’s workspace, privacy, and ability to personalize workspace are predictors for workers’ productivity [26, 27]. The shift from working in a well-established office space to work from home can be challenging for many office workers. Such challenges can be stressful and might negatively affect a worker’s desire to work and thus reduce their productivity. Having the optimal physical setup, proper ergonomics and the necessary equipment is crucial to create an effective workspace that boosts productivity and increases the workers’ engagement with their workstation. In their analysis of the workforce shift to the WFH, Moretti et al. [28] explained that workers are expected to engage extensively with their workstations while working from home, and therefore presented their suggestions for a comfortable workstation (i.e., an adjustable desk and chair to prevent back and joints pain, along with a footrest, and an adjustable monitor screen).

Research also shows that separating the workspaces from living spaces is an important factor when working remotely. It is recommended to have a dedicated workspace to create physical boundaries, help workers establish a productive work atmosphere, increase workers’ desire to stay longer hours at their workstation and signal to other household members that they do not want to be distracted [29]. Yet, when the space at home is limited and several members of the household need a space to work, sharing the same workspace might become inevitable. In fact, in a survey conducted by Suart et al. [30], it was found that only 48.6%of the respondents had a dedicated workspace, 31%were sharing their workspace with others and the remaining 20.4%were working in a variety of places in their homes. However, research shows that productivity decreases with lack of ability to adjust/personalize workspace as well as lack of storage space [31]. On the other hand, Rudnicka et al. [32] conducted a survey about the WFH during the COVID-19 era and found that some respondents felt that constantly changing the workspace helped them focus and enhanced their work performance.

In addition to the workspace, indoor environmental quality (IEQ) (e.g., lighting, temperature, ventilation, air quality, noise) also plays a major role in creating a comfortable work experience [33]. Research studies that investigated the effect of IEQ parameters on worker performance suggest that the more satisfied the workers are with the IEQ, the more productive they are [34, 35], an effect that is stronger in private offices as opposed to shared offices [36]. In fact, increasing daylight illumination in office spaces can increase workers’ performance by 13%while also reducing fatigue [37]. Beyond lighting, improvement in indoor air quality and thermal conditions have also been linked to enhanced productivity, as well as increased attention and concentration [38]. Research supports that the more control individuals have over their environments, the more satisfied they are with it [39], thus having access to environmental controls might also improve worker productivity. While one might expect to find a similar relation between IEQ satisfaction and productivity during WFH, it is important to investigate this relation for home offices. Following this review, we investigated the effect of the workspace characteristics on the WFH experience during the pandemic.

Research question 2: Do workspace characteristics (having a dedicated space, sharing the workspace with others, availability of office furniture/equipment, and satisfaction with the indoor environmental quality factors) affect workers’ productivity and the time spent at workstation when work is performed from home during the pandemic period?

2.3Work context

Another important consideration in worker productivity and work experience when WFH is the ability of workers to set and maintain appropriate boundaries between work duties and house responsibilities. With role conflicts, workers used to find it challenging to manage work and family/life commitments, even before the pandemic era [40, 41]. With workers shifting to WFH abruptly with the pandemic, new forms of conflicts between work and life occurred. When working and living in the same space, setting boundaries between the work and life becomes more challenging. For example, the sense of time might fade in the homogeneous work-home environment and workers might elongate their working hours, start working earlier, later or on the weekends [42]. Some workers might embrace the flexibility in their work hours while other workers might have no choice but to schedule their working hours around their household members or responsibilities [43].

At the same time, the unclear boundaries between home and office might have increased work expectations [44]. For example, Peasley et al. [45] found that sales personnel felt burned out when trying to meet the management’s expectations and they believed that job expectations became higher as soon as they started working from home during the COVID-19 pandemic. With this increase in expectations, workers might be tasked with more duties and expected to deliver additional work, increasing working hours and requiring them to spend additional time at their workstations [46]. Meanwhile, communication among different parties (e.g., coworkers, supervisors, employers), which is crucial to keep the remote workers productive, might be impaired as communication during the pandemic usually comes in one form: virtual and is limited by personal, organizational and technological means. Yet, communication provides work resilience by sustaining the usual work operations and is key to mitigating the undesired effects of the sudden shift to WFH [47]. Thus, the impact of work adjustments and work expectations on workers’ productivity and work engagement needs to be further investigated, which leads to the following research question:

Research question 3: Do work conditions (adjustment to work schedules, communication with coworkers, workload expectations distractions while working, having other family members at home, the support or lack of from employer) affect workers’ productivity and the time spent at workstation when work is performed from home during the pandemic period?

The following sections of the paper are organized as follows: The methods section explains in detail the data collection procedure and describes the control, dependent and independent variables selected based on the framework adopted in this study. The results section provides a summary of the statistical analysis conducted with focus on relative impact of variables within each of the three pillars. Finally, the discussion section offers a detailed interpretation of the study findings and identifies key considerations for implementing WFH as an organizational strategy in future work.

3.1Respondents

An online questionnaire was designed and administered through Qualtrics Panel Services for 45-days from April 27, 2020 to June 11, 2020. The study was reviewed by the Institutional Review Board of the University of Southern California and was approved as exempt research (UP-20-00339 IRB study number). Following initial email and public posts to social media pages, 1,409 respondents completed the survey voluntarily without any compensation. Of the 1,409 responses, 91 responses were screened out because the individuals were not working from home at a workstation during the COVID-19 pandemic. Furthermore, 330 responses were excluded because the respondents completed less than a quarter of the survey. Our final sample consisted of the remaining 988 respondents (558 women, 317 men, 113 unreported) who ranged from 18 years old to 80 years old (M = 40.9 years, SD = 13.1 years). Respondents were primarily Caucasian (60.9%), followed by Asian (24.6%), Hispanic or Latinx (9.3%), Black (2.8%), and another race or ethnicity (2.4%). Responses were received from 40 states, with the most respondents from California (47.3%); 6.4%of responses were from outside of the U.S. and 10.5%of respondents did not provide their location. Level of education was distributed with 28.6%of the respondents having a 4-years college degree or less, 37.2%having a graduate or professional degree, and the remaining 34.2%having a doctorate degree. Most respondents indicated being employed full-time (82.8%) as opposed to working part-time, being students, or contract workers.

3.2Procedure

After consenting, respondents were asked “Does your job require you to use a workstation (e.g., desk, computer terminal, laptop) most of the day, and are you working (or have worked) from home due to COVID-19 or a stay-at-home mandate?” Only respondents who indicated “yes” to this question continued; others were told that they were not eligible for the study and thanked. Eligible respondents continued to complete the survey. Demographic measures included gender, age, location, and employment status and the other demographic control variables described below as “worker characteristics.” Respondents reported their perceived level of productivity and indicated the difference in time spent at a workstation both compared to pre-pandemic levels. These two items served as our dependent variables of work outcome as described below. In addition, respondents answered numerous questions related to their workspace and their work context. All questions were optional. Upon reaching the end of the survey, respondents were thanked for their participation and asked to share a link to the anonymous survey with others who were working from home during the COVID-19 pandemic.

3.3Measures

The survey comprised of multiple categories, with questions asking about the worker demographics, workspace characteristics, work conditions and a final category investigating the work performance and time spent at the workstation when work is performed from home during the pandemic period. First, worker characteristics were examined and used as control variables in our analyses. Second, respondents provided information related to the context of their physical workspace and how they conducted work within their new WFH situations. These context items were examined as independent variables in our analyses. Specifically, we examined individual and controlled multivariate contributions to two dependent variables that served as indicators of work performance during the pandemic’s WFH period as compared to pre-pandemic work performance.

3.4Data analysis

We examined, in turn, the impact of the three types of predictor variables on our work performance outcomes (i.e., relative productivity and change in time spent at a workstation): worker characteristics, workspace context, and work context. For each type of predictors, we conducted descriptive analyses, followed by individual tests of each predictor using t-test and Pearson correlations. Finally, linear regression models were used to examine the individual predictors (within a type) against all other variables in terms of predicting unique variance in the two work performance outcomes. Specifically, within each regression model, we used: (1) the worker characteristic control variables as predictors, (2) the worker characteristic control variables and -in a second step- the workspace context variables as predictors, or (3) the worker characteristic control variables and -in a second step- the work context variables as predictors. Within the regression models, dummy coding was used for all categorical variables. “Business and office” was used as the reference category for occupation because it was the most frequently selected, and “less than $50K” served as the reference category for income because the median income for the U.S. falls in this category [48].

4.1Worker characteristics

Respondents had an average age of 40.90 years (SD = 13.10 years). Female workers accounted for 56.5%of the respondents, whereas 32.1%of respondents were male workers and the remaining 11.4%were missing or preferred not to say. Respondents primarily worked in occupations categorized as business and office (29.1%), engineering and architecture (24.6%), education and arts (22.1%), followed by respondents in healthcare and social services (9.3%), computer sciences and mathematics (8.2%), basic scientists (4.2%), and services and physical occupations (2.6%). The largest percentage of respondents reported annual income between $50 K and $100 K (40.6%), with the remaining respondents almost evenly distributed among the three remaining income levels: less than $50 K (19.0%), $100 K –$150 K (21.7%), and more than $150 K (18.8%). The average physical health status was (M = 2.84, SD = 0.87) and the mental health status was (M = 2.70, SD = 0.93).

We first tested for any differences in productivity levels and difference in time spent at the workstation based on worker characteristics. There was a significant effect of gender on relative productivity (female M = 2.94, SD = 1.17 vs. male M = 2.78, SD = 1.15; t(864)=-1.97, p = 0.050) but not on the change in time spent at the workstation (female M = 1.65 hours (99 minutes), SD = 2.95 vs. male M = 1.37 hours (82.2 minutes), SD = 2.95; t(851) = –1.33, p = 0.181). Age correlated positively and minimally (albeit significantly) with relative productivity (r = 0.12, N = 746, p < 0.001), while the correlation between age and change in time spent at the workstation was not significant (r = 0.06, N = 736, p = 0.122).

Mean and standard deviations for relative productivity and change in time at a workstation among the occupational categories and income levels are presented in Table 1. Significant differences across occupational categories were noted in both relative productivity (F(6, 847) = 3.28, p = 0.003) and change in time spent at the workstation (F(6, 835) = 4.90, p < 0.001). Post hoc comparisons using the Tukey HSD suggest that relative productivity was significantly lower for respondents with jobs in “engineering and architecture” (M = 2.74, SD = 0.08, p = 0.011), “computer sciences and mathematics” (M = 2.77, SD = 0.14, p = 0.032), and “healthcare and social services” (M = 2.70, SD = 0.13, p = 0.026) as compared with increases reported by those in the “scientists” category (M = 3.50, SD = 0.2). Although workers across occupational categories all reported an increase in the time spent at a workstation after WFH, individuals with jobs in “healthcare and social services” (M = 2.63, SD = 0.33) reported a change in time that was significantly higher than those in “engineering and architecture” (M = 1.25, SD = 0.20, p = 0.021) and “business and office” (M = 1.05, SD = 0.18, p = 0.012) categories. Individuals in the “business and office” category showed a significantly lower change in time at a workstation as compared to individuals in “education and arts” (M = 2.05, SD = 0.22, p = 0.012).

While no difference by income category was noted in the change in time spent at the workstation (F(3, 764) = 1.60, p = 0.191), relative productivity differed across income levels (F(3,777) = 3.47, p = 0.021). Post hoc analyses show that productivity was significantly lower for workers earning less than $50 K (M = 2.64, SD = 0.10) than those earning $50 K–$100 K (M = 3.00, SD = 0.06, p = 0.011) or $100K–$150K (M = 2.98, SD = 0.09, p = 0.050). Finally, while workers’ productivity significantly positively correlated with both physical (r = 0.22, N = 880, p < 0.001) and mental health (r = 0.35, N = 881, p < 0.001) statuses, correlations between the change in time spent at the workstation and physical health (r = –0.03, N = 861, p = 0.362) or mental health (r = –0.06, N = 862, p = 0.072) were not significant.

To answer our first research question, regression analysis was employed to understand the effect of workers characteristics on the WFH experience during the pandemic. The results suggest that worker characteristics, measured in this survey, significantly predicted respondents’ relative productivity (F(13, 720) = 10.58, p < 0.001, R² = 0.16) and change in time spent at workstation (F(13, 705) = 2.31, p = 0.005, R² = 0.10). Results for individual characteristics included in the model are presented in Table 2. Controlling for all the other worker characteristic variables, there was a significant difference in relative productivity between workers in the “healthcare and social services” and those in the reference of “business and office” (b = –0.38, p = 0.003). Age (b = 0.01, p = 0.005), physical health status, (b = 0.11, p = 0.029) and mental health status (b = 0.36, p < 0.001) each uniquely predicted a significant increase in workers’ productivity during the WFH period. A positive effect of working in the “healthcare and social services” also existed for change in time spent at a workstation (b = 0.92, p = 0.007), along with a negative effect of mental health status (b = –0.30, p = 0.024) once controlling for all the other worker characteristic variables.

4.2Workspace context

Table 3 presents the mean, standard deviation (SD) of the continuous predictors, and the frequency and percentage of the categorical ones for the workspace context related variables.

An Analysis Of Variance (ANOVA) revealed a significant effect of the type of workspace on relative productivity (F(2, 857) = 3.49, p = 0.031). Tukey HSD (Honestly Significant Test) tests showed that productivity for “I have a dedicated space for work activities” (M = 3.00, SD = 0.07) was significantly higher than that of “I work in a variety of places” (M = 2.69, SD = 0.09, p = 0.021), but the “I have a dedicated space with other uses” (M = 2.89, SD = 0.05) did not statistically differ from the other two conditions. There was also a significant omnibus effect on the difference in time spent at workstation (F(2,931) = 3.40, p = 0.032). Specifically, difference in time for the “I have a dedicated space for work activities” (M = 1.75 hours (105 minutes), SD = 0.17) was statistically higher than that of “I work in a variety of places” (M = 0.98 hours (59 minutes), SD = 0.24, p = 0.021), but the “I have a dedicated space with other uses” (M = 1.48, SD = 0.14) was not significantly different from the two other conditions.

An independent t-test revealed that workers who do not have other people present (M = 3.01, SD = 1.15) were significantly more productive than those who do have (M = 2.80, SD = 1.14; t(841) = 2.25, p = 0.011). There was no difference in time spent at workstation based on whether respondents have people present while working or not (t(911) = 0.21, p = 0.846).

Differences in relative productivity and change in time spent at the workstation based on individual workspace components are presented in Table 4. Independent t-tests reveal that no item of equipment showed a significant relationship with worker productivity. On the other hand, having a regular desk (t(955) = –2.57, p = 0.010) or an adjustable office chair (t(955) = –2.43, p = 0.015) had a significant relationship with change in time spent at workstation. Specifically, workers who had a regular desk (M = 1.73 hours (103 minutes), SD = 2.90) or an adjustable office chair (M = 1.72 hours (102 minutes), SD = 3.00) engaged more at their workstation compared with those who did not have such a desk (M = 1.23 hours (74 minutes), SD = 3.06) or an adjustable chair (M = 1.25 hours (75 minutes), SD = 2.97). Finally, weak correlations were noted among satisfaction with the visual environment (r = 0.10, N = 884, p = 0.003), satisfaction with the thermal environment (r = 0.14, N = 879, p < 0.001), satisfaction with air quality (r = 0.07, N = 881, p = 0.034), satisfaction with noise (r = 0.09, N = 877, p = 0.008) and relative productivity, and no significant correlations existed between these parameters and change in time at the workstation.

To answer our second research question, regression models examining workspace context factors as predictors of relative productivity (F(37,687) = 4.38, p < 0.001, R² = 0.20) and change in time spent at workstation (F(37, 672) = 1.57, p = 0.021, R² = 0.03) were both significant (Table 5). While controlling worker characteristics and all other workspace variables, reporting of “I have a dedicated room for work activities” (b = 0.16, p = 0.020) uniquely predicted significantly higher relative productivity than before the WFH transition. In contrast, reporting “I work in a variety of spaces” (b = –0.16, p = 0.022) uniquely predicted significantly lower relative productivity. Despite low individual correlation to the outcome, once controlling for all other factors, satisfaction with the thermal environment predicted greater worker productivity (b = 0.12, p = 0.024). No individual workspace variables were unique predictors of the difference in time spent at the workstation.

4.3Work context

Table 6 presents the mean and standard deviation (SD) of the continuous predictors, and the frequency and percentage of the categorical predictors for the work context related variables, and Table 7 presents evaluation of differences in relative productivity and change in time spent at the workstation based on work context variables. Respondents who adjusted their work hours (earlier or later work schedule, switched days of week for work, shorter/longer) showed a significant increase (t(955) = –3.23, p < 0.001) in time spent at the workstation (M = 1.65, SD = 3.01) compared to those who did not adjust their hours (M = 0.94, SD = 2.90). Furthermore, respondents who purchased new items for themselves showed a significantly higher change in time spent at the workstation (M = 1.88, SD = 2.96) compared with those who did not (M = 1.24, SD = 2.99).

Additional independent t-tests, presented in Table 7, revealed workers with at least one teenager living at home reported higher relative productivity (M = 3.10, SD = 1.15) than those without a teenager at home (M = 2.86, SD = 1.18; t(644) = 2.02, p = 0.044), although both means hovered around the neutral response of 3.0. Respondents with school age children at home showed a significantly larger increase (t(721) = –2.02, p = 0.044) in time spent at the workstation (M = 1.97 hours (118.2 minutes), SD = 3.19), in comparison to those who do not have school age children at home (M = 1.42 hours (85.2 minutes), SD = 2.98). Likewise, not having a toddler (t(692) = 2.58, p = 0.010) at home seemed to lessen the increase in time spent at the workstation during WFH: respondents with a toddler at home increased their hours at the workstation less (M = 0.76 hours (45.6 minutes), SD = 3.48) than those without a toddler at home (M = 1.58 hours (94.8 minutes), SD = 2.95). Finally, workers with an infant at home (M = 2.92, SD = 1.17) were significantly less productive than those without an infant at home (M = 2.39, SD = 1.37; t(600) = –3.16, p = 0.001), and respondents with an infant in the home increased their hours at the workstation less (M = 0.64 hours (38.4 minutes), SD = 3.81) than those without an infant at home (M = 1.53 hours (94.8 minutes), SD = 3.00; t(661) = –2.15, p = 0.032). Additional t-tests were conducted to analyze the effect of purchasing new items on productivity and change in time spent at the workstation. The respondents who purchased new items for themselves showed a significantly higher difference in time spent at the workstation (M = 1.88, SD = 2.96) compared with those who did not (M = 1.24, SD = 2.99).

We used correlations to evaluate the relationship between our outcome variables and the work context predictors that are continuous (i.e., communication with coworkers, workload expectations or requirements, and distractions while working). Communication with coworkers had a moderate positive association with relative productivity (r = 0.46, N = 881, p < 0.001), as did workload expectations (r = 0.32, N = 882, p < 0.001). In contrast, distractions while working was moderately negatively associated relative productivity (r = –0.41, N = 881, p < 0.001). Although significant, distractions while working (r = –0.07, N = 860, p = 0.035) and communication with coworkers (r = 0.09, N = 860, p = 0.013) had essentially no relationship to change in time spent at workstation. Only workload expectations or requirements (r = 0.21, N = 860, p < 0.001) had a weak positive association with change in time at the workstation.

The third research question was answered by two regression models examining work context variables as predictors of work performance outcomes were significant: relative productivity (F(29, 444) = 14.96, p < 0.001, R² = 0.50) and change in time spent at workstation (F(29, 439) = 2.43, p < 0.001, R² = 0.14). The results are presented in Table 8. Controlling for the other variables, communication with coworkers uniquely predicted higher relative productivity (b = 0.26, p < 0.001), as did workload expectations (b = 0.30, p < 0.001). In contrast, in this multiple regression, distractions while working (b = –0.26, p < 0.001) and having an infant at home (b = –0.61, p = 0.001) both independently predicted lower relative productivity. Likewise, adjusting work hours (starting earlier/later, switching days of week, having shorter/longer workdays) uniquely predicted larger increases in change in the time at the workstation (b = 0.81, p = 0.012), as did workload expectations (b = 0.40, p = 0.003). Finally, distractions while working also independently predicted smaller differences in hours at the workstation (b = –0.24, p = 0.031).

5.1Theoretical implications

5.2Practical implications

Increased WFH arrangements will likely be a reality beyond the COVID-19 pandemic. Conclusions from this study provide an nuanced understanding of how the WFH experience can impact work performance, which provides employers, employees, and other supporting professionals (e.g., ergonomists, therapists) with information relative to the key considerations for how to mitigate factors that might degrade performance. Importantly, our findings indicate that better physical and mental health statuses were associated with improved productivity, highlighting the need to develop supportive policies and practices targeted to key worker and work characteristics that will assist in balancing performance and well-being in the WFH context. For example, in our data, older, higher-income, and female workers demonstrated higher productivity levels compared to their counterparts, which highlights types of workers who may require increased supports through organizational policies and practices to promote successful WFH (e.g., younger, lower-income, male). These findings are similar to other recent reports published by the Pew Research Center noting that older workers did not find the transition to WFH during the pandemic as difficult as younger workers [78], and workers with low income levels faced more financial distress compared to those earning high income levels [79].

In addition to worker characteristics, our results provide employers and other professionals with information related to the way in which work is conducted and the space where work is completed when WFH. For example, communication with coworkers was associated with increased productivity levels which indicates the necessity for organizations to identify communication tools that can foster collaboration during remote work. Additionally, our findings inform recommendations to establish the WFH workstation to improve work performance, which includes identifying a dedicated space for work purposes, isolating the worker from other individuals in the household, and creating an environment with optimal thermal conditions to boost performance. These data illuminate potential equity issues, as many workers may not have the capability or resources necessary to create an ideal WFH environment. These issues must be considered and addressed through policies, practices, and supports as organizations consider widespread adoption of WFH practices.

Finally, our results indicate a shift in the way in which workers are engaging in their work; specifically, that workers are spending approximately 90 additional minutes engaging at their office workstations as part of their workday. Prior to the pandemic, musculoskeletal pain was among the most widespread health issues threatening office workers [80]. On a positive note, our results indicated that workers who spent more time at their workstation tended to own an office desk or an adjustable chair. With WFH likely resulting in more time spent at the workstation than when working in the office setting, organizations should prioritize support for an ergonomic set up of home workstations. Furthermore, we also found that increased time at the workstation was associated with shifts in the way work hours were scheduled, and decreased time at the workstation was reported when there was an infant or a toddler at home. Employers and supporting professionals must be sensitive to individual employee needs or desires to shift work patterns to support work-family balance, develop methods to monitor employee performance and well-being relative to any shifts in work patterns, and identify policies, practices, or supports that will ensure successful re-organization of work patterns to promote positive WFH experiences.

5.3Limitations

Multiple limitations must be considered when interpreting the findings in this study. Firstly, caution should be used in generalizing the results of this study. The vast majority of respondents to this survey were from the U.S., and nearly half of the respondents were from California. In addition, although respondents were well distributed across income categories, our overall sample had a relatively high income as compared to the median income level in the U.S., which is below $50 K. Moreover, our sample also had a much larger proportion of the respondents having college or graduate degrees than typical across all people in the U.S. Secondly, productivity in this study was measured relative to the productivity prior to work from home experience due to the pandemic. Slight variations up and down may or may not result in meaningful changes in the context of the actual job performance. Finally, the change in time spent at the workstation does not necessarily equate to overall increase in work hours, but is an indication that the workers are spending more time at their workstations when WFH than when conducting work at their place of employment.

5.4Future research directions

Further research is needed to study the specific effects of WFH on gender inequalities both at home and at work. Along those lines, additional research is also needed to examine the effect of family-work conflicts and the mechanisms used by workers to cope with distractions caused by other family members and daily home tasks. Furthermore, it is necessary to investigate the role of communication technology on the WFH experience and to understand the practical and social implications of relying on digital technologies to perform WFH. In addition, future research is needed to continue investigating the feasibility of WFH across different work categories and what role technology plays in enabling successful transitions to different occupations. Finally, the health status of workers working from home should be given additional attention and researchers should investigate ways to promote healthy work conditions and proper means to balance between work and well-being.