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• Published: 12 June 2024

Hybrid working from home improves retention without damaging performance

• Nicholas Bloom   ORCID: orcid.org/0000-0002-1600-7819 1   na1 ,
• Ruobing Han   ORCID: orcid.org/0000-0001-9126-5503 2   na1 &
• James Liang 3 , 4  

Nature volume  630 ,  pages 920–925 ( 2024 ) Cite this article

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Working from home has become standard for employees with a university degree. The most common scheme, which has been adopted by around 100 million employees in Europe and North America, is a hybrid schedule, in which individuals spend a mix of days at home and at work each week 1 , 2 . However, the effects of hybrid working on employees and firms have been debated, and some executives argue that it damages productivity, innovation and career development 3 , 4 , 5 . Here we ran a six-month randomized control trial investigating the effects of hybrid working from home on 1,612 employees in a Chinese technology company in 2021–2022. We found that hybrid working improved job satisfaction and reduced quit rates by one-third. The reduction in quit rates was significant for non-managers, female employees and those with long commutes. Null equivalence tests showed that hybrid working did not affect performance grades over the next two years of reviews. We found no evidence for a difference in promotions over the next two years overall, or for any major employee subgroup. Finally, null equivalence tests showed that hybrid working had no effect on the lines of code written by computer-engineer employees. We also found that the 395 managers in the experiment revised their surveyed views about the effect of hybrid working on productivity, from a perceived negative effect (−2.6% on average) before the experiment to a perceived positive one (+1.0%) after the experiment. These results indicate that a hybrid schedule with two days a week working from home does not damage performance.

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Principal component analysis

Working from home (WFH) surged after the COVID-19 pandemic, with university-graduate employees typically WFH for one to two days a week during 2023 (refs. 2 , 6 ). Previous causal research on WFH has focused on employees who are fully remote, usually working on independent tasks in call-centre, data-entry and helpdesk roles. This literature has found that the effects of fully remote working on productivity are often negative, which has resulted in calls to curtail WFH 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 . However, there are two challenges when it comes to interpreting this literature. First, more than 70% of employees WFH globally are on a hybrid schedule. This group comprises more than 100 million individuals, with the most common working pattern being three days a week in the office and two days a week at home 2 , 8 , 9 . Second, most employees who are regularly WFH are university graduates in creative team jobs that are important in science, law, finance, information technology (IT) and other industries, rather than performing repetitive data-entry or call processing tasks 10 , 11 .

This paper addresses the gap in previous studies in two key ways. First, it uses a randomized control trial to examine the causal effect of a hybrid schedule in which employees are allowed to WFH two days per week. Second, it focuses on university-graduate employees in software engineering, marketing, accounting and finance, whose activities are mainly creative team tasks.

Our study describes a randomized control trial from August 2021 to January 2022, which involved 1,612 graduate employees in the Airfare and IT divisions of a large Chinese travel technology multinational called Trip.com. Employees were randomized by even or odd birthdays into the option to WFH on Wednesday and Friday and come into the office on the other three days, or to come into the office on all five days.

We found that in the hybrid WFH (‘treatment’) group, attrition rates dropped by one-third (mean control  = 7.20, mean treat  = 4.80, t (1610) = 2.02, P  = 0.043) and work satisfaction scores improved (mean control  = 7.84, mean treat  = 8.19, t (1343) = 4.17, P  < 0.001). Employees reported that WFH saved on commuting time and costs and afforded them the flexibility to attend to occasional personal tasks during the day (and catch up in the evenings or weekends). These effects on reduced attrition were significant for non-managerial employees (mean control  = 8.59, mean treat  = 5.33, t (1215) = 2.23, P  = 0.026), female employees (mean control  = 9.19, mean treat  = 4.18, t (568) = 2.40, P  = 0.017) and those with long (above-median) commutes (mean control  = 6.00, mean treat  = 2.89, t (609) = 1.87, P  = 0.062).

At the same time, we found no evidence of a significant effect on employees’ performance reviews, on the basis of null equivalence tests, and no evidence of a difference in promotion rates over periods of up to two years (‘Null results’ section of the Methods ). We did find significant differences in pre-experiment beliefs about the effects of WFH on productivity between non-managers and managers. Before the experiment, managers tended to have more negative views, reporting that hybrid WFH would be likely to affect productivity by −2.6%, whereas non-managers had more positive views (+0.7%) ( t (1313) = −4.56, P  < 0.001). After the experiment, the views of managers increased to +1.0%, converging towards non-managers’ views (mean non-manager  = 1.62, mean manager  = 1.05, t (1343) = −0.945, P  = 0.345). This highlights how the experience of hybrid working leads to a more positive assessment of its effect on productivity—consistent with the overall experience in Asia, the Americas and Europe throughout the pandemic, where perceptions of WFH improved considerably 13 .

The experiment

The experiment took place at Trip.com, the third-largest global travel agent by sales in 2019. Trip.com was established in 1999, was quoted on NASDAQ in 2003 and was worth about US$20 billion at the time of the experiment. It is headquartered in Shanghai, with offices across China and internationally, and has roughly 35,000 employees.

In the summer of 2021, Trip.com decided to evaluate the effects of hybrid WFH on the 1,612 engineering, marketing and finance employees in the Airfare and IT divisions, spanning 395 managers and 1,217 non-managers. All experimental participants were surveyed at baseline, with questions on expectations, background and their interest in volunteering for early participation in the experiment. The firm randomized employees with an odd-number birthday (born on the first, third, fifth and so on day of the month) into the treatment group.

Figure 1 shows two pictures of employees working in the office to highlight three points. First, in the second half of 2021, COVID incidence rates in Shanghai were so low that employees were neither masked nor socially distanced at the office. Although the COVID pandemic had led to lockdowns in early 2020 and during 2022, during the second half of 2021, Shanghai employees were free to come to work, and typically were unmasked in the office. Second, employees worked in modern open-plan offices in desk groupings of four or six colleagues from the same team, reflecting the importance of collaboration. Third, the office is a large modern building, similar to many large Asian, European and North American offices.

Pictures of Trip.com employees in the office during the experiment. The people in the experimental sample are typically in their mid-30s, and 65% are male. All of them have a university undergraduate degree and 32% have a postgraduate degree, usually in computer science, accounting or finance, at the master’s or PhD level. They have 6.4 years tenure on average and 48% of employees have children (Extended Data Table 1 ).

Effects on employee retention

One key motivation for Trip.com in running the experiment was to evaluate how hybrid WFH affected employee attrition and job satisfaction. The net effect was to reduce attrition over the experiment by 2.4%, which against the control-group base of 7.2% was a one-third (33%) reduction in attrition (mean control  = 7.20, mean treat  = 4.80, t (1610) = 2.02, P  = 0.043). Consistent with this reduction in quit rates, employees in the treatment group also registered more positive responses to job-satisfaction surveys (mean control  = 7.84, mean treat  = 8.19, t (1343) = 4.17, P  < 0.001). Employees were anonymously surveyed on 21 January 2022, and employees in the treatment group showed significantly higher scores on a scale from 0 (lowest) to 10 (highest) in ‘work–life balance’, ‘work satisfaction’, ‘life satisfaction’ and ‘recommendation to friends’, and significantly lower scores in ‘intention to quit’ (Extended Data Table 2 ).

One possible explanation for the lower quit rates in the treatment group is that quit rates in the control group increased because the individuals in this group were annoyed about being randomized out of the experiment. However, quit rates in the same Airfare and IT divisions were 9.8% in the six months before the experiment—higher than the rate for the control group during the experimental period. Quit rates over the experimental period in the two other Trip.com divisions for which we have data (Business Trips and Marketing) were 10.5% and 9.8%—again higher than that for the control group during the experimental period. This suggests that, if anything, the control-group quit rates were reduced rather than increased by the experiment, possibly because some of them guessed (correctly) that the policy would be rolled out to all employees once the experiment ended.

Figure 2 shows the change in attrition rates by three splits of the data. First, we examined the effect on attrition for the 1,217 non-managers and 395 managers separately. We saw a significant drop in attrition of 3.3 percentage points for the non-managers, which against a control-group base of 8.6% is a 40% reduction (mean control  = 8.59, mean treat  = 5.33, t (1215) = 2.23, P  = 0.026). By contrast, there was an insignificant increase in attrition for managers (mean control  = 2.96, mean treat  = 3.13, t (393) = −0.098, P  = 0.922). We also found that non-managers were more enthusiastic before the experiment, with a volunteering rate of 35% (versus 22% for managers), matching the media sentiment that although non-managerial employees are enthusiastic about WFH, many managers are not ( t (1610) = 4.86, P  < 0.001).

Data on 1,612 employees’ attrition until 23 January 2022. Top left, all employees. Only 1,259 employees filled out the baseline survey question on commuting length, so the commute-length (two ways) sample is for 1,259 employees. Sample sizes are 820 and 792 for control and treatment; 1,217 and 395 for non-managers and managers; 570 and 1,042 for women and men; and 648 and 611 for short and long commuters, respectively. Two-tailed t -tests for the attrition difference within each group between the control and treatment groups are (difference = 2.40, s.e. = 1.18, confidence interval (CI) = [0.0748, 4.72], P  = 0.043) for all employees; (difference = 3.26, s.e. = 1.46, CI = [0.392, 6.12], P  = 0.026) for non-managers; (difference = −0.169, s.e. = 1.73, CI = [−3.57, 3.23], P  = 0.922) for managers; (difference = 5.01, s.e. = 2.08, CI = [0.915, 9.10], P  = 0.017) for women; (difference = 0.997, s.e. = 1.43, CI = [−1.82, 3.81], P  = 0.487) for men; (difference = 2.61, s.e. = 1.93, CI = [−1.19, 6.41], P  = 0.178) for employees with median (90 min, two-way) or shorter commutes; and (difference = 3.11, s.e. = 1.66, CI = [−0.156, 6.37], P  = 0.062) for above-median (90 min, two-way) commuters.

Second, we examined the effect on attrition by total commute length, splitting the sample into people with shorter and longer total commutes on the basis of the median commute duration (two-way commutes of 1.5 h or less versus those exceeding 1.5 h, with 648 and 611 employees, respectively). We found that there was a larger reduction in quit rates (52%) for those with a long commute (mean control  = 6.00, mean treat  = 2.89, t (609) = 1.87, P  = 0.062). The reduction in quit rates was similarly large for employees with a long commute if we instead defined a long commute as a two-way commute time exceeding 2 h (mean control  = 7.33, mean treat  = 1.89, t (307) = 2.31, P  = 0.021). Employees who volunteered to take part in the experiment had longer one-way commute durations (Extended Data Table 3 ; mean non-volunteer  = 0.80, mean volunteer  = 0.89, t (1257) = −3.68, P  < 0.001). This is not surprising given that the most frequently cited benefit of WFH is no commute 1 .

Third, we examined the effect on attrition by gender, examining the 570 female and 1,042 male employees separately. We found that there was a 54% reduction in quit rates for female employees (mean control  = 9.2, mean treat  = 4.2, t (568) = 2.40, P  = 0.017). For male employees, there was an insignificant 16% reduction in quit rates (mean control  = 6.15, mean treat  = 5.15, t (1040) = 0.70, P  = 0.487). This greater reduction in quit rates among female individuals echoes the findings of previous studies 6 , 14 , 15 , 16 , which suggest that women place greater value on remote work than men do. Notably, although the treatment effect of WFH was significantly larger for female employees, volunteers were less likely to be female (mean non-volunteer  = 0.37, mean volunteer  = 0.32, t (1610) = −2.02, P  = 0.043); this might suggest that women have greater concerns about negative career signalling by volunteering to WFH.

Employee performance and promotions

Another key question for Trip.com was the effect of hybrid WFH on employee performance. To assess that, we examined four measures of performance: six-monthly performance reviews and promotion outcomes for up to two years after the start of the experiment, detailed performance evaluations, and the lines of code written by the computer engineers. We also collected self-assessed productivity effects of hybrid working from experimental participants before and after the experiment to evaluate employee perceptions.

Performance reviews are important within Trip.com as they determine employees’ pay and career progression, so are carefully conducted. The review process for each employee is built on formal assessments provided by their managers, co-workers, direct reports and, if appropriate, customers. They are reviewed by employees, collated by managers and by the human resources team, and then discussed between the manager and the employee. This lengthy process takes several weeks, providing a well-grounded measure of employee performance. Although these reviews are not perfect, given their tight link to pay and career development, both managers and employees put a large amount of effort into making these informative measures of performance.

Figure 3 reports the distribution of performance grades for treatment and control employees for the four half-year periods: July to December 2021, January to June 2022, July to December 2022 and January to June 2023. These four performance reviews span a two-year period from the start of the experimental period. Across all review periods, we found no difference in reviews between the treatment and control groups (Extended Data Table 4 and ‘Null results’ section of the Methods ).

Results from performance reviews of 1,507 employees in July–December 2021, 1,355 employees in January–June 2022, 1,301 employees in July–December 2022 and 1,254 employees in January–June 2023. Samples are lower over time owing to employee attrition from the original experimental sample. Two-tailed t -tests for the performance difference within each period between the control and treatment groups, after assigning each letter grade a numeric value from 1 (D) to 5 (A), are (difference = 0.056, s.e. = 0.043, CI = [−0.029, 0.14], P  = 0.198) for July–December 2021; (difference = 0.034, s.e. = 0.044, CI = [−0.0529, 0.122], P  = 0.440) for January–June 2022; (difference = −0.019, s.e. = 0.046, CI = [−0.11, 0.072], P  = 0.677) for July to December 2022; and (difference = 0.046, s.e. = 0.051, CI = [−0.054, 0.146], P  = 0.369) for January–June 2023. The null equivalence tests are included in the ‘Null results’ section of the Methods .

Figure 4 reports the distribution of promotion outcomes for the treatment and control employees for the same periods. We see no evidence of a difference in promotion rates across treatment and control employees. This is an important result given the evidence that fully remote working can damage employee development and promotions 14 , 17 , 18 .

Promotion outcomes for 1,522 employees in July–December 2021, 1,378 employees in January–June 2022, 1,314 employees in July–December 2022 and 1,283 employees in January–June 2023. Samples are lower over time owing to employee attrition from the original experimental sample. Two-tailed t -tests for the promotion difference within each period between the control and treatment groups are (difference = −0.86, s.e. = 1.34, CI = [−3.51, 1.74], P  = 0.509) for July–December 2021 promotions; (difference = 0.12, s.e. = 0.85, CI = [−1.54, 1.78], P  = 0.892) for January–June 2022 promotions; (difference = −0.51, s.e. = 1.12, CI = [−2.72, 1.70], P  = 0.651) for July–December 2022 promotions; and (difference = −0.99, s.e. = 1.02, CI = [−2.99, 1.00], P  = 0.328) for January–June 2023 promotions. The null equivalence tests are included in the ‘Null results’ section of the Methods .

We also analysed the effects of treatment on performance grades and promotions for a variety of subgroups, including managers, employees with a manager in the treatment group, longer-tenured employees, longer-commuting employees, women, employees with children, computer engineers and those living further away, as well as looking at whether internet speed had any effect. We found no evidence of a difference in response to treatment across these groups (Extended Data Table 5 ).

The experiment also analysed two other measures of employee performance. First, the performance reviews at Trip.com have subcomponents for individual activities such as ‘innovation’, ‘leadership’, ‘development’ and ‘execution’ (nine categories in all) when these are important for an individual employee’s role. We collected these data and analysed these scores for the four six-month performance review periods. We found no evidence of a difference across these nine major categories over the four performance review periods (Extended Data Table 6 ). This indicates that for categories that involve softer skills or more team-focused activities—such as development and innovation—there is no evidence for a material effect of being randomized into the hybrid WFH treatment. Second, for the 653 computer engineers, we obtained data on the lines of code uploaded by each engineer each day. For this ‘lines of code submitted’ measure, we found no difference between employees in the control and treatment groups (Extended Data Fig. 1 and ‘Null results’ section of the Methods ).

Self-assessed productivity

All experiment participants were polled before the experiment in a baseline survey on 29 and 30 July 2021, which included a two-part question on their beliefs about the effects of hybrid WFH on productivity. Employees were asked ‘What is your expectation for the impact of hybrid WFH on your productivity?’, with three options of ‘positive’, ‘about the same’ or ‘negative’. Individuals who chose the answer ‘positive’ were then offered a set of options asking how positive they felt, ranging from [5% to 15%] up to [35% or more], and similarly so for negative choices. For aggregate impacts we took the mid-points of each bin, and 42.5% for >35% and –42.5% for <−35%. Employees were resurveyed with the same question after the end of the experiment on 21 January 2022.

The left panel of Fig. 5 shows that employees’ pre-experimental beliefs about WFH and productivity were extremely varied. The baseline mean was –0.1%, but with widespread variation (standard deviation of 11%). This spread should be unsurprising to anyone who has been following the active debate about the effects of remote work on productivity. At the end-line survey conducted on 21 January 2022, the mean of these beliefs had significantly increased to 1.5%, revealing that the experience of hybrid working led to a small improvement in average employee beliefs about the productivity impact of hybrid working (mean baseline  = −0.06%, mean endline  = 1.48%, t (2658) = −3.84, P  < 0.001). This could be because hybrid WFH saves employees commuting time and is less physically tiring, and, with intermittent breaks between group time and quiet individual time, can improve performance 19 , 20 , 21 , 22 .

Sample from 1,315 employees (314 managers, 1,001 non-managers) at the baseline and 1,345 employees (324 managers, 1,021 non-managers) at the end line. Two-tailed t -tests for the difference in productivity expectations between baseline and end line, after assigning a numeric value corresponding to the midpoint of the bucket, are (baseline mean = −0.058, end-line mean = 1.48, difference = −1.54, s.e. = 0.40, CI = [−2.33, −0.753], P  < 0.001). Two-tailed t -tests for the baseline difference between the productivity expectations of managers and non-managers are (difference = −3.28, s.e. = 0.72, CI = [−4.69, −1.86], P  < 0.001), and the t -tests for the end-line difference are (difference = −0.571, s.e. = 0.604, CI = [−1.76, 0.615], P  = 0.345).

The right panel of Fig. 5 shows that in the baseline survey, managers were negative about the perceived effect of hybrid work on their productivity, with a mean effect of −2.6%. Non-managers, by contrast, were significantly more positive, at +0.7% in the baseline survey (mean non-manager  = 0.7%, mean manager  = −2.6%, t (1313) = −4.56, P  < 0.001). At the end of the experiment, the views of managers improved to 1.0%, with no evidence of a difference from the non-managers’ mean value of 1.6% (mean non-manager  = 1.62%, mean manager  = 1.05%, t (1343) = −0.95, P  = 0.345). Hence, the experiment led managers to positively update their views about how hybrid WFH affects productivity, and to more closely align with non-managers.

Of note, we saw that employees in the treatment and control groups had similar increases in self-assessed productivity (difference 0.58%, s.d. = 0.59%). Employees from four other divisions in Trip.com were also polled about the productivity impact of hybrid WFH after the end of the experiment in March 2022, with a mean estimate of +2.8% on a sample of 3,461 responses—similar to the 1.5% end line for the experimental sample. This suggests that even close exposure to hybrid WFH is sufficient for employees to change their views, consistent with previous evidence of a positive society-wide shift in perceptions about WFH productivity after the 2020 pandemic 8 .

Once the experiment ended, the Trip.com executive committee examined the data and voted to extend the hybrid WFH policy to all employees in all divisions of the company with immediate effect. Their logic was that each quit cost the company approximately US$20,000 in recruitment and training, so a one-third reduction in attrition for the firm would generate millions of dollars in savings. This was publicly announced on 14 February 2022, with wide coverage in the Chinese media. Since then, other Chinese tech firms have adopted similar hybrid policies 23 .

This highlights how, contrary to the previous causal research focused on fully remote work, which found mostly negative effects on productivity 5 , 6 , 7 , hybrid remote work can leave performance unchanged. This suggests that hybrid working can be profitably adopted by organizations, given its effect on reducing attrition, which is estimated to cost about 50% of an individual’s annual salary for graduate employees 24 . Hybrid working also offers large gains for society by providing a valuable amenity (perk) to employees, reducing commuting and easing child-care 6 , 25 , 26 .

The experiment was conducted in a Chinese technology firm based in Shanghai. Although it might not be possible to replicate these results perfectly in other situations, Trip.com is a large multinational firm with global suppliers, customers and investors. Its offices are modern buildings that look similar to those in many American, Asian and European cities. Trip employees worked 8.6 h per day on average, close to the 8 h per day that is usual for US graduate employees 27 . The business had a large drop in revenue in 2020 (see Extended Data Fig. 4 ), followed by roughly flat revenues through the 2021 experiment period into 2022, so this was not a period of exceptionally fast or slow growth. As such, we believe that these results— that is, the finding that allowing employees to WFH two days per week reduces quit rates and has a limited effect on performance—would probably extend to other organizations. Also, this experiment analysed the effects of working three days per week in the office and two days per week from home. So, our findings might not replicate to all other hybrid work arrangements, but we believe that they could extend to other hybrid settings with a similar number of days in the office, such as two or four days a week. We are not sure whether the results would extend to more remote settings such as one day a week (or less) in the office, owing to potential challenges around training, innovating and culture in fully remote settings.

Finally, we should point out two implications of the experimental design. First, full enrolment into hybrid schemes is important because of concerns that volunteering might be seen as a negative signal about career ambitions. The low volunteer rate among female employees, despite their high implied value (from the large reductions in quit rates observed), is particularly notable in this regard. Second, there is value in experimentation. Before the experiment, managers were net-negative in their views on the productivity impact of hybrid working, but after the experiment, their views became net-positive. This highlights the benefits of experimentation for firms to evaluate new working practices and technologies.

Location and set-up

Our experiment took place at Trip.com in Shanghai, China. In July 2021, Trip.com decided to evaluate hybrid WFH after seeing its popularity amongst US tech firms. The first step took place on 27 July 2021, when the firm surveyed 1,612 eligible engineers, marketing and finance employees in the Airfare and IT divisions about the option of hybrid WFH. They excluded interns and rookies who were in probation periods because on-site learning and mentoring are particularly important for those individuals. Trip.com chose these two divisions as representative of the firm, with a mix of employee types to assess any potentially heterogeneous impacts. About half of the employees in these divisions are technical employees, writing software code for the website, and front-end or back-end operating systems. The remainder work in business development, with tasks such as talking to airlines, travel agents or vendors to develop new services and products; in market planning and executing advertising and marketing campaigns; and in business services, dealing with a range of financial, regulatory and strategy issues. Across these groups, 395 individuals were managers and 1,217 non-managers, providing a large enough sample of both groups to evaluate their response to hybrid WFH.

Randomization

The employees were sent an email outlining how the six-month experiment offered them the option (but not the obligation) to WFH on Wednesday and Friday. After the initial email and two follow-up reminders, a group of 518 employees volunteered. The firm randomized employees with odd birthdays—those born on the first, third, fifth and so on of the month—into eligibility for the hybrid WFH scheme starting on the week of 9 August. Those with even birthdays—born on the second, fourth, sixth and so on of the month—were not eligible, so formed the control group.

The top management at the firm was surprised at the low volunteer rate for the optional hybrid WFH scheme. They suspected that many employees were hesitating because of concerns that volunteering would be seen as a negative signal of ambition and productivity. This is not unreasonable. For example, a previous study 28 found in the US firm they evaluated that WFH employees were negatively selected on productivity. So, on 6 September, all of the remaining 1,094 non-volunteer employees were told that they were also included in the program. The odd-birthday employees were again randomized into the hybrid WFH treatment and began the experiment on the week of 13 September. In this paper we analyse the two groups together, but examining the volunteer and non-volunteer groups individually yields similar findings of reduced quit rates and no impact on performance.

Employee characteristics and balancing tests

Figure 1 shows some pictures of employees working in the office (left side). Employees all worked in modern open-plan offices in desk groupings of four or six colleagues from the same team. By contrast, when WFH, they usually worked alone in their apartments, typically in the living room or kitchen (see Extended Data Fig. 2 ).

The individuals in the experimental sample are typically in their mid-30s. About two-thirds are male, all of them have a university undergraduate degree and almost one-third have a graduate degree (typically a master’s degree). In addition, nearly half of the employees have children (details in Extended Data Table 1 ).

In Extended Data Table 7 we confirm that this sample is also balanced across the treatment and control groups, by conducting a two-sample t -test. The exceptions are from random variation given that the sampling was by even or odd day-of-month birthday—the control sample is 0.5 years older ( P  = 0.06), and this is presumably linked to why those in this group have 0.06% more children ( P  = 0.02) and 0.4 years more tenure ( P  = 0.09).

In Extended Data Table 3 , we examine the decision to volunteer for the WFH experiment. We see that volunteers were significantly less likely to be managers (mean non-volunteer  = 0.28, mean volunteer  = 0.17, t (1610) = −4.85, P  < 0.001) and had longer commute times (hours) (mean non-volunteer  = 0.80, mean volunteer  = 0.89, t (1257) = 3.68, P  < 0.001). Notably, we don’t find evidence of a relationship between volunteering and previous performance scores (mean non-volunteer  = 3.81, mean volunteer  = 3.81, t (1580) = −0.02, P  = 0.985), highlighting, at least in this case, the lack of evidence for any negative (or positive) selection effects around WFH.

Extended Data Fig. 3 plots the take-up rates of WFH on Wednesday and Friday by volunteer and non-volunteer groups. We see a few notable facts. First, take-up overall was about 55% for volunteers and 40% for non-volunteers, indicating that both groups tended to WFH only one day, typically Friday, each week. At Trip.com, large meetings and product launches often happen mid-week, so Fridays are seen as a better day to WFH. Second, the take-up rate even for non-volunteers was 40%, indicating that Trip.com’s suspicion that many employees did not volunteer out of fear of negative signalling was well-founded, and highlighting that amenities like WFH, holiday, maternity or paternity leave might need to be mandatory to ensure reasonable take-up rates. Third, take-up surged on Fridays before major holidays. Many employees returned to their home towns, using their WFH day to travel home on the quieter Thursday evening or Friday morning. Finally, take-up rates jumped for both treatment-group and control-group employees in late January 2022 after a case of COVID in the Shanghai headquarters. Trip.com allowed all employees at that point to WFH, so the experiment effectively ended early on Friday 21 January. The measure of an employee’s daily WFH take-up excludes leave, sick leave or occasions when they cannot come to the office owing to extreme bad weather (typhoon) or to the COVID outbreak in the company.

Null results

To interpret the main null results, we conduct null equivalence tests using the two one-sided tests (TOST) procedure in R (refs. 29 , 30 ). This test required us to specify the smallest effect size of interest (SESOI). For the results pertaining to performance review measures, we use 0.5 as the SESOI. This corresponds to half of a consecutive letter grade increase or decrease, because we had assigned numeric values to performance letter grades in increments of 1, with the lowest letter grade D being 1, and the highest letter grade A being 5. We performed equivalence tests for a two-sample Welch’s t -test using equivalence bounds of ±0.5. The TOST procedure yielded significant results using the default alpha of 0.05 for the tests against both the upper and the lower equivalence bounds for the performance measures for July–December 2021 ( t (1504) = −10.20, P  < 0.001)), January–June 2022 ( t (1353) = −10.57, P  < 0.001)), July–December 2022 ( t (1299) = 10.34, P  < 0.001)) and January–June 2023 ( t (1248) = −8.80, P  < 0.001)). The equivalence test is therefore significant, which means we can reject the hypothesis that the true effect of the treatment on performance is larger than 0.5 or smaller than −0.5. So, we interpret the performance effects of the treatment to be actually null on the basis of the SESOI we used, as opposed to no evidence of a difference in performance.

We conducted null equivalence results for the effect of the treatment on promotions using 2 as the SESOI, corresponding to ±2 percentage points (pp) difference in promotion rates. Although we can reject the null hypothesis that the true effect of treatment on promotion is larger than 2 pp or smaller than −2 pp in January–June 2022 ( t (1376) = −2.22, P  = 0.013) and July–December 2022 ( t (1306) = 1.33, P  = 0.092), we fail to reject the null equivalence hypothesis in July–December 2021 ( t (1513) = 0.83, P  = 0.203) and January–June 2023 ( t (1250) = 0.98, P  = 0.163). Thus, we interpret the results on promotion as no evidence of a difference between promotion rates across treatment and control employees.

We also conducted the equivalence test for lines of code using 29 lines of code per day as the SESOI, which corresponds to 10% of the mean number of lines of code for the control group. We arrive at this SESOI on the basis of rounding down the productivity effects of previous findings 8 , 10 . We can reject the equivalence null hypothesis for lines of code ( t (92362) = −2.74, P  = 0.003)) so we interpret the effect of the treatment as a null effect.

Volunteer versus non-volunteer groups

In the main paper we pool the volunteer and non-volunteer groups. In Extended Data Table 5 we examine the impacts on performance and promotions and we see no evidence of a difference in performance and promotion treatment effects for volunteer versus non-volunteer groups (column 9).

Performance subcategories

The company has a rigorous performance-reviewing process every six months that determines employees’ pay and promotion, so is carefully conducted. The review process for each employee is built on formal reviews provided by their managers, project leaders and sometimes co-workers (peer review). Managers are more like an employee’s direct managers for organizational purposes, but for a particular project, the project leader could be another higher-level employee. In such a case, the manager of the employee would ask that project leader for an opinion on the employee’s contribution to the project. An individual’s overall score is a weighted sum of scores from various subcategories that managers have broad flexibility over defining, because tasks differ across employees, and managers would give a score for each task. For example, an employee running a team themselves will have subcategories around developing their direct reports (leadership and communication), whereas an employee running a server network will have subcategories around efficiency and execution. The performance subcategory data come from the text of the performance review. We first used the most popular Chinese word segmentation package in Python, named Jieba, to identify the most frequent Chinese words from task titles across four performance reviews. We also removed meaningless words and incorporated common expressions such as key performance indicators (‘KPI’), objectives and key results (‘OKR’), ‘rate’ and ‘%’. This process resulted in a total of 236 unique words and expressions. We then manually categorized those most frequent keywords into nine major subcategories (see below) by meanings and relevance. Finally, on the basis of the presence of keywords in the task title, tasks were grouped into the following subcategories:

Communication tasks are those that involve communication, collaboration, cooperation, coordination, participation, suggestion, assistance, organization, sharing and relationships.

Development tasks are those that involve coding or codes, data or datasets, systems, techniques and skills.

Efficiency tasks are those that involve cost reduction, ratios, return on investment (ROI), rate, %, improvement, growth, lifting, adding, optimizing, profit, receiving, gross merchandise value (GMV), OKR, KPI, work and goal.

Execution tasks are those that involve execution, conducting, maintenance, delivery, output, quality, contribution and workload.

Innovation tasks are those that involve development, R&D and innovation.

Leadership tasks are those that involve leadership, managing or management, approval, internal, strategy, coordination and planning.

Learning tasks are those that involve learning, growing, maturing, talent, ability, value competitiveness and personal improvement.

Project tasks are those that involve project, supply, product, business line, cooperation and clients.

Risk tasks are those that involve risk, compliance, supervision, recording and monitoring, safety, rules and privacy.

Data sources

Data were provided by a combination of Trip.com sources, including human resources records, performance reviews and two surveys. All data were anonymized and coded using a scrambled individual ID code, so no personally identifiable information was shared with the Stanford team. The data were drawn directly from the Trip.com administrative data systems on a monthly basis. Gender is collected by Trip.com from employees when they join the company.

The full sample has 1,612 experiment participants, but we have 1,507, 1,355, 1,301 and 1,254 employees, respectively, in the subsamples for the four performance reviews from July–December 2021, January–June 2022, July–December 2022 and January–June 2023. These smaller samples are due to attrition. In addition, for the first performance review in July–December 2021, 105 employees did not have sufficient pre-experiment tenure to support a performance review (they had joined the firm less than three months before the experimental draw). The review text data covers 1,507,1,339,1,290 and 1,246 people, as some employees do have an overall score and review text but do not have additional and task-specific scores. The reason is that these employees do not have the full range of all tasks, so their managers did not write the full review script. For the two surveys, Trip.com used Starbucks vouchers to incentivize response and collected responses from 1,315 employees (314 managers, 1,001 non-managers) at the baseline on the left, and that of 1,345 employees (324 managers, 1,021 non-managers) at the end line.

All tests used two-sided Student t -tests unless otherwise stated. Analysis was run on Stata v17 and v18, R version 4.2.2. Unless stated otherwise, no additional covariates are included in the tests. The null hypothesis for all of the tests excluding null equivalence tests is a coefficient of zero (for example, zero difference between treatment and control).

Inclusion and ethics statement

The design and execution of the experiment was run by Trip.com. No participants were forced to WFH owing to the experiment (the entire firm was, however, forced to WFH during the pandemic lockdown). The treatment sample had the option but not the obligation to WFH on Wednesday or Friday. The experiment was designed, initiated and run by Trip.com. N.B. and R.H. were invited to analyse the data from the experiment, with consent for data collection coming from Trip.com internally. The experiment was exempt under institutional review board (IRB) approval guidelines because it was designed and initiated by Trip.com, before N.B. and R.H. were invited to analyse the data. Only anonymous data were shared with the Stanford team. Trip.com based the experimental design and execution on their previous experience with WFH randomized control trials 17 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data necessary to reproduce the primary results of this study can be found at https://doi.org/10.7910/DVN/6X4ZZL . These data have been anonymized and split into individual files to ensure that no individual is identifiable. All figures and tables can be replicated using this data.

Code availability

The code necessary to reproduce the primary results of this study can be found at https://doi.org/10.7910/DVN/6X4ZZL .

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Acknowledgements

We thank the Smith Richardson Foundation for funding; J. Cao, T. Zhang, S. Ye, F. Chen, X. Zhang, Y. He, J. Li, B. Ye and M. Akan for data, advice and logistical support; D. Yilin for research assistance; S. Ayan, S. Buckman, S. Gurung, M. Jackson and P. Lambert for draft feedback; and J. Sun for project leadership.

Author information

These authors contributed equally: Nicholas Bloom, Ruobing Han

Authors and Affiliations

Department of Economics, Stanford University, Stanford, CA, USA

Nicholas Bloom

Shenzhen Finance lnstitute, School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, China

Ruobing Han

National School of Development, Peking University, Beijing, China

James Liang

Trip.com, Shanghai, China

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Contributions

N.B. oversaw the analysis, presented the results and wrote the main drafts of the paper. He was the principal investigator on the research grant supporting the research. R.H. supervised data collection and analysed the data, presented the results and helped to draft the paper. J.L. initiated and designed the study, discussed the results and analysis and facilitated the Trip.com engagement. N.B. and R.H. are co-first authors.

Corresponding authors

Correspondence to Nicholas Bloom , Ruobing Han or James Liang .

Ethics declarations

Competing interests.

No funding was received from Trip.com. J.L. is the co-founder, former CEO and current chairman of Trip.com, with equity holdings in Trip.com. No other co-author has any financial relationship with Trip.com. Neither the results nor the paper was pre-screened by anyone. The experiment was registered with the American Economic Association on 16 August 2021 after the experiment had begun but before N.B. and R.H. had received any data. Only anonymous data were shared with the Stanford team.

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Extended data figures and tables

Extended data fig. 1 wfh had no effect on lines of code written..

The data coves the experimental period starting on 9 August 2021 for the first wave and 13 September for the second wave, running to 23 January 2022, for both waves. Lines of code submitted per day is available for 653 employees whose primary role was writing code, spanning a total of 95,494 days. Lines are those uploaded to trip.com on a daily basis. Data plotted on a log-2 scale for readability. Reported P value is calculated using a two-sided t -test on the number of code lines and the difference is for control minus treatment. When using log 2 (code lines) the difference has a P value of 0.750 (noting the sample is 27,605 days because of dropping 0 values). When using log 2 (1 + code lines) the difference has a P value of 0.0103, with treatment having the higher average values. The null equivalence tests are included in the ‘Null results’ section of the Methods .

Extended Data Fig. 2 Home (October 2021).

Employees set up basic working environments in their living rooms, studies, or kitchens, and bring back company laptops if necessary.

Extended Data Fig. 3 Take-up rate for WFH treatment and control by volunteer status.

Data for 1,612 employees from 9 August 2021 (volunteers) and 13 September (non-volunteers) to 23 January 2022. Public holidays, personal holidays and excused absence (for example, sick leave) are excluded. Take-up rate is percentage of Wednesday and Friday each week they WFH.

Extended Data Fig. 4 Trip.com revenues.

Trip.com revenues from 2000 to 2023.

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Bloom, N., Han, R. & Liang, J. Hybrid working from home improves retention without damaging performance. Nature 630 , 920–925 (2024). https://doi.org/10.1038/s41586-024-07500-2

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• Published: 30 November 2020

A rapid review of mental and physical health effects of working at home: how do we optimise health?

• Jodi Oakman   ORCID: orcid.org/0000-0002-0484-8442 1 ,
• Natasha Kinsman 1 ,
• Rwth Stuckey 1 ,
• Melissa Graham 1 &
• Victoria Weale 1  

BMC Public Health volume  20 , Article number:  1825 ( 2020 ) Cite this article

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Metrics details

The coronavirus (COVID-19) pandemic has resulted in changes to the working arrangements of millions of employees who are now based at home and may continue to work at home, in some capacity, for the foreseeable future. Decisions on how to promote employees’ health whilst working at home (WAH) need to be based on the best available evidence to optimise worker outcomes. The aim of this rapid review was to review the impact of WAH on individual workers’ mental and physical health, and determine any gender difference, to develop recommendations for employers and employees to optimise workers’ health.

A search was undertaken in three databases, PsychInfo, ProQuest, and Web of Science, from 2007 to May 2020. Selection criteria included studies which involved employees who regularly worked at home, and specifically reported on physical or mental health-related outcomes. Two review authors independently screened studies for inclusion, one author extracted data and conducted risk of bias assessments with review by a second author.

Twenty-three papers meet the selection criteria for this review. Ten health outcomes were reported: pain, self-reported health, safety, well-being, stress, depression, fatigue, quality of life, strain and happiness. The impact on health outcomes was strongly influenced by the degree of organisational support available to employees, colleague support, social connectedness (outside of work), and levels of work to family conflict. Overall, women were less likely to experience improved health outcomes when WAH.

Conclusions

This review identified several health outcomes affected by WAH. The health/work relationship is complex and requires consideration of broader system factors to optimise the effects of WAH on workers’ health. It is likely mandated WAH will continue to some degree for the foreseeable future; organisations will need to implement formalised WAH policies that consider work-home boundary management support, role clarity, workload, performance indicators, technical support, facilitation of co-worker networking, and training for managers.

Peer Review reports

The current global pandemic caused by coronavirus disease 2019 (COVID-19) has resulted in an unprecedented situation with wide ranging health and economic impacts [ 1 , 2 ]. The working environment has been significantly changed with thousands of jobs lost and women impacted at higher rates than men [ 3 , 4 ]. For those employed in sectors able to work remotely, mostly white-collar professional workers, their homes have now become their workplace, school, and place for relaxation. As economies begin to reopen with resumption of some normal activities, questions arise about the potential return to formal office environments and the implications for employees whilst COVID-19 remains active in the community [ 5 ]. Many organisations will continue mandating working at home (WAH) for the foreseeable future to avoid making COVID-19 regulation related changes to their office environments [ 6 ].

The emergence of new technologies has revolutionised working patterns, enabling work from anywhere for many employees [ 7 , 8 ]. The concept of telework has existed since the 1970s but in a more limited scope than is currently possible [ 7 ]. The extensive availability of technology has enabled location and timing of work to be undertaken with significant flexibility, offering benefits to employers and employees. However, to date there is no universally accepted definition of telework. The International Labour Organisation (ILO) defines telework as the use of information and communications technologies (ICTs) including smartphones, tablets, laptops or desktop computers for work that is performed outside the employer’s premises [ 7 ]. A range of positive benefits are associated with teleworking, including improved family and work integration, reductions in fatigue and improved productivity [ 9 ]. However, the blurring of physical and organisational boundaries between work and home can also negatively impact an individual’s mental and physical health due to extended hours, lack of or unclear delineation between work and home, and limited support from organisations [ 10 ]. The mandatory WAH situation is complex and requires a systematic examination to identify the impact of organisational, physical, environmental and psychosocial factors on individuals’ mental and physical health.

The ongoing need for containment of COVID 19 and continued need to undertake WAH requires evidence synthesis to develop policies and guidelines to protect employees’ health and well-being. We undertook a rapid review of the evidence on the impact of WAH on individual workers’ mental and physical health. In addition, we examined any gender differences of these impacts. We considered the body of evidence to develop recommendations for employers to optimise the health of their employees.

Search strategy and selection criteria

This rapid review was undertaken using principles recommended by the WHO [ 11 ]. PRISMA reporting guidelines were followed [ 12 ]. The search strategy was developed in consultation with a senior librarian and, for this rapid review, was limited to three databases. ProQuest (Central, Coronavirus Research Database, Social Science Premium Collection, Science Database), PsycINFO and Web of Science databases were searched on 5 May 2020. The search strategy was limited to English language, peer reviewed journal articles published from January 2007 onwards. To ensure wide capture of the literature, study design was not restricted. The date limit was selected to ensure the contemporary work environment was captured. The year 2007 was when the first smartphone was released, this technology change enabled greater flexibility in relation to work arrangements. To ensure the search strategy addressed the research questions two broad concepts were included, those relating to WAH (e.g., “home work”, “telecommute”) and health-related outcomes (e.g., “musculoskeletal risk”, “mental health”). Refer to Appendix for the full search strategy.

For inclusion in the current rapid review, studies were required to focus on adult white collar/professional employees WAH during business hours, and to include mental or physical health related outcomes of workers. Studies were excluded if they focused on domestic workers, self-employed workers, informal working from home (working from home after hours to catch up on work), productivity outcomes, chronic illness/disability, or pregnancy/breast feeding. The rationale of the search strategy was to capture studies which included participants who undertook working from home on a regular basis, but these arrangements did not necessarily have to be mandated or formalised by the organisation.

Titles, abstracts and full texts were screened by two authors using Covidence [ 13 ]. Disagreements were resolved by consensus. Reasons for exclusion of studies were noted. The outcomes of interest were measurable changes in physical or mental health. Secondary analysis was undertaken for studies which reported differences by gender.

Data extraction and quality assessment

Data extraction was undertaken using a standardised form and included setting, study design, method used, details of participants, industry setting, measures used, and the health outcomes. The risk of bias assessment was used as a proxy for the quality of the study and undertaken for both qualitative and quantitative studies using separate forms. The risk of bias domains were derived from the RTI research bank, Cochrane Collaboration tool quality assessment, and the Johanna Briggs appraisal tool for qualitative research [ 14 , 15 , 16 ]. Each potential source of bias was assessed as high, moderate, low, or unclear risk with justification given for judgement. In line with rapid review principles, data extraction and risk of bias for each article was undertaken by at least one author, with a sub sample screened by a second author for accuracy.

An overall quality assessment of each study was determined using a previously published rating system [ 17 ]. Studies with a 'low' risk rating for the confounding factors criteria and a higher number of ‘low’ risks than ‘high’ or ‘unclear’ risks, were deemed to have a 'low' overall risk of bias. Studies with a 'high' risk rating for the confounding factors criteria and more ‘low’ risks than ‘high’ or ‘unclear’ risks, were assessed as having 'moderate' overall risk of bias. Studies with a 'high' risk of bias rating for confounding factors criteria, and more ‘high’ or ‘unclear’ risks than ‘low’ risks were designated to have a 'high' overall risk of bias.

Data analysis

Qualitative data were organised using narrative synthesis to identify how WAH influenced employees physical and mental health. Studies were grouped by broad health outcomes and then a separate analysis by gender undertaken.

The database search identified 1557 papers of which 21 met the inclusion criteria. Two additional studies were included following a reference list search of the articles which met the inclusion criteria, making a total of 23 studies. The primary reason for exclusion was the study did not include a health outcome. The PRISMA diagram outlines the screening process (see Fig.  1 ). The studies represented 10 countries (USA, UK, Australia, New Zealand, Japan, Belgium, South Africa, Brazil, Germany, The Netherlands), and varied in study design: 20 cross sectional, one cohort, one controlled before and after, and one combined cross sectional and cohort (refer Table  1 ). No randomised trials were identified. Studies included 19 quantitative, 3 qualitative and 1 mixed methods.

PRISMA diagram

Studies were conducted in the following industry sectors: government departments and agencies (five), financial services (three), technology (two), academia (one), telecommunications (one), logistics (one). Ten studies used data from surveys of the general public or did not focus on a particular industry sector. The number of hours and nature of WAH arrangements varied between studies; participants WAH either full time (two studies [ 21 , 36 ] or part-time, and had access to a formal WAH policy or ad hoc WAH approval by managers. Only one study examined employees undertaking mandatory WAH [ 36 ]. Some studies did not specify the nature of the WAH arrangements. Due to the heterogenous nature of the studies, it was not possible to conduct a meta-analysis.

Health related outcomes

Physical health related outcomes ( n  = 3) identified in the studies included: pain, self-reported health and perceived safety. Mental health related outcomes ( n  = 7) identified included: well-being, stress, depression, fatigue, quality of life, strain and happiness. Seven studies undertook separate gender analysis (see Table  2 ).

Risk of bias

Following assessment of risk of bias, quantitative studies were rated as: four high risk, three moderate risk, and 13 low risk. For the qualitative studies ( n  = 3) the overall risk of bias for all studies was assessed as moderate. The four studies with high risk of bias included cross sectional surveys [ 18 , 22 , 26 , 31 ]. For the cohort studies, quantitative [ 29 ], qualitative [ 36 ] and mixed methods [ 39 ] were utilised, with moderate and low risk of bias, respectively (see Tables  3 and 4 ).

Physical health-related impacts

Three studies explored the physical health impacts of WAH [ 22 , 23 , 32 ]; one of these will be discussed in the section on gender. Filardí [ 22 ] surveyed government employees who reported that ‘I feel safer working from home’, but the WAH arrangements were not clearly defined. In contrast, a study by Nijp et al. [ 32 ] found WAH had a negative impact on physical health. This study measured self-reported health in a control and an intervention group of finance company employees, before and after implementation of a policy to enable part-time WAH. Participants reported a small but statistically significant decrease in self-reported health which could not be explained as usual health indicators and job demands remained unchanged.

Mental health-related impacts

The majority of studies (21 studies) explored the effect of working at home on mental health. Fourteen are explored in this section and seven studies that included a gender analysis are presented separately.

The impacts of WAH on mental health were complex. Nine studies considered environmental, organisational, physical, or psychosocial factors in the relationship between WAH and mental health [ 18 , 20 , 21 , 24 , 25 , 31 , 33 , 35 , 38 ]. Working at home could have negative or positive impacts, depending on various systemic moderators such as: the demands of the home environment, level of organisational support, and social connections external to work.

Five studies [ 20 , 25 , 33 , 35 , 38 ] examined the influence of colleagues and organisational support on WAH. Suh & Less [ 35 ] compared the effect of technostress (defined as work overload, invasion of privacy, and role ambiguity) on IT company employees doing low intensity WAH (< 2.5 days per week), to those doing high intensity WAH (> 2.5 days per week). Low intensity WAH employees experienced higher strain associated with work overload and invasion of privacy, related to IT complexity, pace of IT change, lower job autonomy, and being constantly in electronic contact with work. Bentley et al. [ 20 ] explored the influence of organisational (social and manager) support on health outcomes of WAH employees and found a similar relationship between lower levels of organisational support and higher psychological strain. Sardeshmukh et al. [ 33 ] also examined the effects of organisational support (via job resources and demands) and found associations between WAH and less time pressure, less role conflict, and greater autonomy, resulting in less exhaustion. However, they also found WAH was associated with lower social support, lower feedback and greater role ambiguity which increased exhaustion; overall these negative effects did not outweigh the overall positive impact of WAH. Vander Elst et al. [ 38 ] found increased WAH hours were associated with less emotional exhaustion and cognitive stress which was mediated by support from colleagues. Those working more days at home experienced greater emotional exhaustion and cognitive stress associated with reduced social support from their colleagues. Grant et al. [ 25 ] interviewed employees WAH and identified colleagues’ support and communication as important influences on psychological well-being. Tietze et al. [ 36 ] interviewed seven employees WAH on a full-time basis as part of a three-month pilot scheme. Employees reported an improved sense of personal well-being as they were no longer in a stressful office environment.

Anderson [ 18 ] measured the effect of WAH on the mental well-being of government employees (all participants were WAH > 1 day per fortnight), and found WAH had a positive effect on well-being (feeling at ease, grateful, enthusiastic, happy, and proud) with less negative effect on well-being (bored, frustrated, angry, anxious, and fatigued). The study also found individual traits of openness to experience, lower rumination, and greater social connectedness moderated the relationship between WAH and positive well-being, and a strong level of social connectedness (outside of work) was related to a less negative effect on well-being.

Two studies explored the home environment as a mediator for the relationship between WAH and health related outcomes. Work-family conflict (WFC) occurs when the demands of work impinge on domestic and family commitments. Golden’s [ 24 ] study of computer company employees who were WAH for greater periods of time than in the office, found high levels of exhaustion when combined with a high level of WFC. When WFC was low the same employees experienced a low level of exhaustion compared to those WAH only occasionally. Another study [ 31 ], which surveyed employees with dependent-care responsibilities, found an association between WAH and increased energy levels, and decreased stress; WAH acted as a mediator between health-related outcomes and dependent care responsibilities.

Relationships between WAH and the following mental health-related outcomes were examined: stress [ 8 , 19 , 21 , 22 , 23 , 26 , 28 , 29 , 30 , 31 , 34 , 37 , 38 ], quality of life [ 22 , 27 , 37 ], well-being [ 18 , 19 , 25 , 36 , 38 ], and depression [ 8 ]. Five studies [ 19 , 26 , 28 , 31 , 37 ], reported a decrease in stress levels of employees WAH on a part-time basis. One study [ 8 ] explored employees who were WAH either all or part of their work time and found no direct relationship between WAH and levels of stress. In contrast, VanderElst et al. [ 38 ] found WAH was associated with increased stress. Quality of life was enhanced through WAH in two surveys of employees [ 22 , 37 ]. Filardí et al. [ 22 ] included public sector employees but did not report how long employees were WAH. Tustin [ 37 ] included university employees who were WAH for some of the week.

Bosua et al. [ 19 ] studied employees from government, education and private sectors WAH for some of their week and found a greater sense of well-being was reported compared to when working in the office. Of note, participants reported their preference was to combine WAH with some office time so they could connect with colleagues.

Henke et al. [ 8 ] conducted a study within a financial company and compared employees WAH to those not WAH; those WAH less than 8 h per month had statistically lower levels of depression than those not WAH. No statistically significant relationships were identified between depression and greater number of hours WAH.

Four studies examined the direct relationship impact of WAH on fatigue (including exhaustion, tiredness or changes in energy levels) with mixed results [ 28 , 31 , 32 , 37 ]. Two studies [ 31 , 37 ] concluded WAH resulted in decreased levels of fatigue. However, others [ 28 , 32 ] concluded WAH had no effect on levels of fatigue.

The gender differences in health outcomes related to WAH

Seven studies examined outcomes by gender [ 21 , 23 , 27 , 29 , 30 , 34 , 39 ]. Three studies considered complex interactions when examining gender differences in the WAH and health related outcome relationship. Windelar et al. [ 39 ] examined the effect of interpersonal and external interactions on work exhaustion, using WAH as a moderator. They surveyed employees pre and post implementation of a formal WAH policy (study 1) and then compared employees WAH to those based in the office (study 2). Males had higher levels of work exhaustion following the commencement of telework (study 1). Both studies found WAH increased the negative effect of interactions external to the business on work exhaustion. Females WAH reported higher levels of work exhaustion compared to their colleagues who remained at the office (Study 2). Hornung et al. [ 27 ] examined the role of mediators on the relationship between WAH and mental health and gender differences; they surveyed public servants and found increased time WAH improved quality of life through increased autonomy (mediator). However, in a separate gender analysis the relationship was only significant for males. Eddleston & Mulki [ 21 ] reported an increase in job stress for employees WAH full-time. This was mediated by WFC; an inability to disengage from work, and the integration of work into home life, led to higher WFC which was associated with higher job stress. This relationship was moderated by gender with women experiencing greater WFC due to inability to disengage from work, and men experiencing greater WFC due to integration of work into the family domain.

The remaining four studies examined the direct relationship between WAH and health outcomes. Two studies, both using data from the American Time Use Survey, examined physical and mental health outcomes by gender. Gimenez-Nadal et al. [ 23 ] identified participants WAH as those who indicated non-commute days in a diary record. Diary records were followed by a well-being survey, where male teleworkers reported lower pain levels, lower stress, and lower tiredness (p  < 0.05) compared to non-teleworkers; no differences were found between female teleworkers and non-teleworkers. Song & Gao [ 34 ] compared subjective pain when WAH to work at the office, by gender and parental status, and reported no differences. However, fathers who were WAH reported increased stress, and mothers WAH had decreased happiness.

Kim et al. [ 30 ] and Kazekami [ 29 ] examined the direct relationship between fatigue, stress and happiness. Kim et al. [ 30 ] reported males who were WAH regularly had lower levels of fatigue and stress compared to those who did not. For women, WAH was associated with lower stress levels but higher levels of fatigue compared to those not WAH. Kazekami [ 29 ] found that males WAH reported increased stress and happiness whilst no effect was found for females.

Due to the current COVID-19 situation, WAH has been implemented as part of a broad public health measure to prevent the spread of an infectious disease. Although this measure was introduced rapidly, it is likely WAH will remain in place for some time and organisations will utilise this as a strategy to manage the necessary physical distancing requirements to prevent further outbreaks of COVID-19. This rapid review explored the impact of WAH on physical and mental health outcomes to inform the development of guidelines to support employers in creating optimal working conditions. In addition, included studies were examined to explore any gender differences in the relationship between WAH and health.

The majority of studies in the rapid review employed cross-sectional designs and were of variable quality. The definition of WAH and the number of days per week employees were working at home were often unclear. Of the 23 studies identified as relevant to this review, only one investigated the condition of mandatory WAH [ 36 ], the remainder involved workers who were electing to WAH for different but regular time periods across a week. However, evidence from the review does suggest there are some reasonable actions employers can take to support their employees in optimising their working conditions whilst at home. This discussion will outline the physical and mental health outcomes of WAH and then, drawing on these findings, outline implications for practice.

Physical health and WAH was only examined in three studies. The very low number of studies identified could suggest the search strategy was not adequately targeted to capture studies assessing the physical health outcomes of WAH; however, a range of terms associated with musculoskeletal health were included. Grey literature may have offered further insights but was not included in this rapid review. An alternative explanation may be that in cases where employees are working at home for limited time periods, the use of standard guidelines for workstation arrangements have been considered sufficient and deployed to manage the physical health of workers. The limited coverage of physical health outcomes of WAH was not expected. Previous research, in relation to the occupational health of employees, suggests the focus is more typically on the physical aspects of health [ 40 ].

In contrast, the impact of WAH on mental health outcomes was covered by the majority of included studies ( n  = 21). Three of the studies employed longitudinal approaches [ 29 , 36 , 39 ] with mixed results such as increased stress [ 29 ], improved well-being [ 36 ] and gendered impacts on exhaustion levels [ 39 ]. The mixed results and varying quality of the articles does create challenges in drawing out meaningful themes; however, differences in organisational responses and support were identified as important contributors to either increasing or mitigating negative health outcomes (e.g. [ 20 , 25 , 33 , 35 ]). The complexity of the WAH situation received only limited coverage [ 20 ]. An extensive literature exists supporting the important role of the work environment (e.g. leadership, collegial support, job design) on employees’ health [ 41 ]. The translation of this body of work undertaken in conventional office environments has not yet been undertaken in WAH and offers opportunities for further research.

Only one third of the studies ( n  = 7) undertook separate analysis by gender on the impacts of WAH on health. The differences in health impacts may reflect traditional gender roles where males are perceived as ideal citizen workers whose primary focus is work, whilst for women dual roles exist in the work and domestic sphere which remains pervasive in many cultures [ 42 ]. The situation of WAH may challenge the ability to separate these roles, creating conflict due to the lack of physical distance enabled by undertaking work outside of the house. High levels of WFC are associated with negative outcomes, including poor mental and physical health [ 43 , 44 ], and it is plausible that for some females this is exacerbated by the WAH situation, contributing to the higher levels of exhaustion and stress reported by females [ 21 , 39 ] compared to males in WAH and, for some, increased unhappiness [ 34 ].

Implications for practice

Drawing on the evidence from the current rapid review, key themes were identified and are provided here as considerations to assist with developing optimal working conditions for employees WAH, including organisational support, co-worker support, technical support, boundary management support, and addressing gender inequities:

Organisational support

The current pandemic situation has resulted in many sudden and unexpected changes to work practices which potentially create uncertainty for employees, necessitating regular communication to ensure clarity around role expectations, clearly defined performance measures, appropriate workloads, and access to human resources support [ 19 , 24 , 26 , 28 , 33 ]. Systems which optimise regular, reliable, and consistent communication, using methods which are appropriate for employers and employees, need to be negotiated and implemented. In addition, organisations need to provide training and assistance for managers supervising WAH employees [ 22 , 25 , 31 ]. Organisations may also consider financial compensation to employees for costs associated with WAH [ 31 ].

Coworker support

WAH can be isolating with employees feeling disconnected from their managers and colleagues. Systems which facilitate effective formal and informal coworker support are needed. Formal coworker support that occurs in teams when people are collocated, such as sharing of tasks and incidental problem solving, requires facilitation whilst WAH. In the current mandated WAH situation, provision of regular face-face online contact opportunities and social support could replace the day in the office [ 20 , 24 , 32 , 38 ]. In situations where WAH becomes voluntary, employees are likely to benefit from a regular day in the office to maintain networks [ 19 , 32 , 38 ].

Technical support

The sudden and unexpected requirement to undertake technologically dependent work roles within the domestic environment has exposed the need for high quality technology services for those WAH. Effective WAH requires the provision of appropriate equipment and high-quality technology support in conjunction with training in the necessary software and systems needed by an individual [ 19 , 22 , 31 , 37 ].

Boundary management support

Although only one study reported on mandated WAH [ 28 ], other studies investigated the impact on boundaries between work, domestic, and recreational boundaries [ 21 , 24 , 35 ]. To facilitate boundary management, clarity is required in relation to the expectations of working hours to prevent employees feeling as though they are ‘on call 24/7’ [ 30 ]. Strategies to facilitate this could include education of employees and managers on how to more formally develop boundaries between work and family [ 21 ].

Addressing gender inequities

A key policy priority to support WAH should be targeted at the development of adaptable strategies to ensure they meet the nuanced needs of different employees, irrespective of gender or life course stage. Strategies also need to ensure those who choose or are mandated to work at home do not experience negative career consequences, such as not being offered career advancement or training opportunities [ 45 , 46 ].

Study limitations

Strengths and limitations of this review must be considered. Despite this being a rapid review, a systematic procedure for searching and selection of articles was retained. A further strength was the undertaking of a formal quality appraisal along with reference checks of included studies to reduce the likelihood of omitting relevant literature. However, the review was limited to English language peer-reviewed publications and no search of the grey literature was undertaken. We excluded studies which did not contain a health outcome as a separate measure, therefore some studies which were in the domain of working at home but examined other outcomes, such as productivity, were excluded. Only one study on mandatory WAH was identified, hence the inclusion of studies which examined the voluntary situation were retained. Heterogeneity of outcome measures across studies make direct comparisons difficult; as such a meta-analysis was not undertaken. Retention of all study types and methodologies was undertaken to fully capture data on WAH. Due to the time constraints of this review, contact of authors for additional information was not possible. This review has made several recommendations to support employees WAH, based on the reviewed literature; however, caution is warranted in relation to the unknown impact of the mandatory WAH, which is a unique situation. In the interim , consolidation of the available literature is required, along with longitudinal research to identify causal relationships between WAH and health outcomes.

Overall, the findings from this review suggest the impacts of WAH on individuals’ mental and physical health vary considerably. However, despite limitations with a relatively low number of studies, some consistent principles emerge which can be used to support employers in improving working conditions to mitigate the negative effects of WAH, and enhance the positive effects of WAH on employees’ health. At a minimum, opportunity for regular communication between managers and their team and between colleagues are important and help to reduce the negative impacts associated with feeling isolated whilst WAH. In situations where WAH continues to be mandatory, consideration of the impact on the home environment and the financial impacts of being at home on a full-time basis (e.g., increased heating, cooling and telecommunication costs) is required. Some financial compensation may be appropriate for employees to reduce this fiscal burden, although some of these costs may be offset by reduced costs associated with commuting.

Longitudinal research is required, which systematically considers all factors in the relationship between employees and their organisations whilst WAH; this can inform the development of guidelines to facilitate the creation of optimal WAH conditions to reduce any negative impacts of employees’ health and well-being.

Availability of data and materials

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Abbreviations

• Working at home

Work to family conflict

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Acknowledgements

We would like to acknowledge Dr. Sue Gilbert, La Trobe University, who provided her professional services to assist in the database search.

La Trobe University Building Healthy Communities Committee made some financial contribution to the research but were not involved in the study design, data collection/analysis/interpretation, writing of the report or the decision to submit this paper.

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Oakman, J., Kinsman, N., Stuckey, R. et al. A rapid review of mental and physical health effects of working at home: how do we optimise health?. BMC Public Health 20 , 1825 (2020). https://doi.org/10.1186/s12889-020-09875-z

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The present study aims to contribute to the research of future possibility of Work from Home (WFH) during the pandemic times of Covid 19 and its different antecedents such as job performance, work dependence, work life balance, social interaction, supervisor’s role and work environment. A structured questionnaire was adopted comprising of 19 questions with six questions pertaining to work related infrastructure at home. Data was collected from 138 full time employees working from home which revealed the influence of work dependence, work environment and work life balance which were hypothesized to be directly related to the willingness to work from home in future if given an opportunity. Qualitative analysis revealed that job performance, social interaction and supervisor’s role related hypothesis are refuted. The study tries to bridge the gap between the existing research done in past during normal course of time and current pandemic. The current research of WFH during the Covid 19 in employees working from home in India is an attempt to assess the antecedents in current situation. These results have important theoretical and practical implications.

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Introduction

The threat of a Covid 19 cataclysm has greatly increased over the past few months. The recent Covid 19 outbreak has brought the world to a standstill. Soon after the emergency was declared by the World Health Organization (WHO), all the nations including India began to enforce stringent rules of lockdown in order to curtail the spread of the deadly virus. All the offices, schools, manufacturing units, organizations, shopping malls, markets except healthcare and essential services were shutdown with a view to break the chain of spread. World is reeling in the midst of the novel corona-virus (COVID-19) pandemic with fear of rising death toll due to the deadly virus. Soon after WHO declared the COVID 19 as a pandemic, the Government of India has announced a complete lockdown. In this pandemic situation people from all over the world are facing difficulty to do work in the work place. It has advised companies to implement work from home policy for their staff as part of encouraging social distancing to curb spread of novel corona virus infection.

Hypothesis testing results with SEM

Six factor model of work from home

Due to the unprecedented circumstances, the employees from all the sectors have been impacted significantly. The social distancing and the self-isolation measures imposed by the Government has brought basic structural changes in the way employees work in organizations. Work from home these days has become the need of the hour for most of the working population in the contemporary way of work life and has become common for many employees around the globe (Vilhelmson & Thulin, 2016 ). The office workspace is now combined with the personal space. This has brought a mammoth change in the way employees work. The digital transformation and the virtual workspace have made the employees work together despite located in distinct places. The research conducted by Windeler et al. (Windeler et al., 2017 ) shows that maintaining a certain level of social interaction is important for employees’ functioning when they work from home. Extensive research has been done earlier which centred on the influence of work from home on employee performance (Allen et al., 2015 ; Bailey & Kurland, 2002 ; De Menezes & Kelliher, 2011 ; Gajendran & Harrison, 2007 ; Martínez Sánchez et al., 2007 ). Whereas some studies have also shown that working from home leads to better performance (Allen et al., 2015 ; Vega et al., 2014 ), others warn that working from home leads to social and professional isolation that confines knowledge sharing (Crandall & Gao, 2005 ; Arun Kumar & Shekhar, 2020 ) and leads to the intensification of labour (Felstead & Henseke, 2017 ; Kelliher & Anderson, 2009 ).

Previous researchers focused on working from home (Baker et al., 2007 ). Due to strong surge in employment of women and growing dual earners, flexible working has become important for balanced work and personal life (Russell et al., 2009 ). In modern times, employees have started to adopt various technologies to interconnect devices at home. The influence of technology on the routine home life is studied in earlier research (Grinter et al., 2005 ). Innovative technology and telecommunication have increased the possibility of working from the home. Work from home settings for the employee’s quality of working life were discussed in the earlier studies (Shamir & Salomon, 1985 ). The extensive review of literature has revealed that home office has positive influence and traditional office has negative influence on work life balance when job related factors and family related factors in three work settings namely traditional office, virtual office and home office was studied (Hill et al., 2003 ). Research of work from home during pandemic or emergency is limited due to the sudden upheaval it has created in the recent times.

In this paper an attempt is made to study the various factors related to willingness to work from home in future and its impact on performance, supervision, social interactions with teams. This study also attempts to study the relationship between various factors relating to WFH during the pandemic. It even attempts to study the effect of isolation from the physical workspace and the challenges encountered by the employees working in virtual workspace during the pandemic.

The corona virus pandemic popularly known as Covid 19 has left many employees confined to their homes. The present study focuses on the need arising due to corona pandemic across the world which has further restricted movement across different places during the lockdown. During this period the employees were asked to work from home without affecting organization’s productivity at the same time ensuring social distancing measures which were followed during the lockdown. The present study is trying to access the willingness and the future possibility of WFH as a post pandemic measure. This study shows our preparedness for the next level of new normalcy of virtual workspace. As a precautionary measure if there is an additional requirement to further curtail the movement of people or in order to cut down certain costs without effecting the productivity, the organizations may prefer employees to continue work from home. This study helps the organizations to understand the challenges and the preparedness of future contingencies.

Methodology

Respondents and research approach.

In this cross-sectional study, people from India were requested to participate in the study. Respondents were contacted and requested to fill the questionnaire online through google forms in WhatsApp. The participants were assured of anonymity and confidentiality of data. Their prior consent and willingness to participate in survey was taken. Both female and male respondents were included in the study. The study aimed to examine educated and qualified young professionals within the working age group working from home during the Covid 19 crisis. The convenient sampling technique was implied for collecting the data. Respondents were included in this study only if they were willing to respond. In total, more than 200 questionnaires were distributed. 138 of the total respondents accepted to participate in the study. The response rate for the study was calculated to be 70% which is sufficient to conduct the further analysis. All the participants who filled the form were employees working from home due to lockdown restrictions imposed by the nation, in order to break the chain of transmission of novel corona virus (Covid 19). The field work of the study was conducted during June to December 2020. Each section had several questions related to a particular construct. The first section in the questionnaire consisted of the basic demographic information of the participants, which includes age, gender, marital status, children, educational level and whether they were willing and able (whether they had the infrastructure) to work from home.

To provide the current status of WFH during lockdown comprehensively, the respondents were asked to answer the questions divided into 7 parts which are work related infrastructure at home, job performance, work dependence, work life balance, social interactions, supervisor’s role, work environment and willingness to work from home in future (Shown in Appendix Table  1 ).

Work from home practices in pandemic COVID-19 situation demonstrates multifaceted phenomena. The aim of this paper is to gain deeper insight of willingness to work from home post COVID-19. This paper is based on primary data as well as secondary data. The survey method was adopted to conduct the study. Based on the review of literature and the researcher’s understanding of the concept, a structured questionnaire was adopted.

The questionnaire consisted of 6 demographic questions, 6 pertaining to work infrastructure and 19 questions related to the core essence of the study (See Appendix Table 1 ). Questions on work related infrastructure at home was borrowed from the study done by Garg & van der Rijst, 2015 with slight modifications. The reliability of the questionnaire was checked by calculating the Cronbach’s Coefficient Alpha value (See Table 2 ). This value depicts the reliability of a single uni-dimensional latent construct. The Cronbach’s Coefficient Alpha of the overall scale for this study was calculated to be 0.708. A Cronbach’s coefficient alpha value of 0.60 was suggested as threshold for the Cronbach’s alpha reliability and acceptability (Pallant, 2013 ). This confirmed the internal consistency of the current study.

Job Performance

Job Performance was measured using three item scale used by Raghuram et al. ( 2001 ); Sims et al. ( 1976 ). This scale was also used by Garg and van der Rijst ( 2015 ). The sample question for job performance is “The measures of my job performance are clear.” One question pertaining to this has been added by the authors though not in scale as it is relevant for analysis “Employee engagement is more during the lock down”. Each item was measured using 5-point Likert scale with 1 as strongly disagree and 5 as strongly agree. The Cronbach alpha value for Job Performance is 0.75.

Work Dependence

Work dependence was measured using three item scale used in study done by Sims et al. ( 1976 ). The sample item is “My performance does not depend on working with others.” The scale items are anchored with strongly disagree as 1 and strongly agree as 5. The Cronbach alpha value for Work dependence is 0.84.

Work Life Balance

Work life balance during lockdown was measured using three item scale developed for the purpose of study. The sample questions are “Overall I am comfortable” (not considered due to model fit issues), “I am able to balance both work and household during the lock down” and “I feel it is difficult to maintain work life balance as I have to remain available all the time”. Each item was measured using 5-point Likert scale with 1 as strongly disagree and 5 as strongly agree. The Cronbach’s alpha value for this factor is 0.75.

Social Interaction

Social interaction was measured using three item scale used by Raghuram et al. ( 2001 ). This scale was also used by Garg and van der Rijst ( 2015 ). The sample item is “The work-related meetings in my office are adequate to build good working relationships”. The scale is anchored with 1 as strongly disagree and 5 as strongly agree. The Cronbach’s alpha value for this factor is 0.642.

Supervisors Role

Supervisor’s role was measured using three item scale developed for the purpose of study. The sample question is “My superior is very supportive in addressing problems during the lock down”. Each item was measured using 5-point Likert scale with 1 as strongly disagree and 5 as strongly agree. The Cronbach’s alpha value for this factor is 0.781.

Work Environment

Work environment was measured using three item scale used by Fonner and Roloff ( 2010 ). This scale was also used by Garg and van der Rijst ( 2015 ). The sample item is “I am distracted by other things going on in my work environment, such as background noise?”. The scale is anchored with 1 as strongly disagree and 5 as strongly agree. The Cronbach’s alpha value for this factor is 0.66.

Willingness to Work from Home in Future

The dependent variable willingness to work from home in future (FWFH) post covid crisis was measured using single item “I feel post pandemic also work from home permits should be given”. This was measured using 5-point Likert scale with 1 as strongly disagree and 5 as strongly agree.

Data Synthesis

To test the hypothesized model, a Structural Equation Model (SEM) was used. The Statistical Package for Social Sciences (SPSS 28) and Analysis of Moment Structures (AMOS 28) was used for the study. The research analysis was conducted using two-step approach. Measurement model and Structural models were tested. The measurement model was checked for validity, internal consistency and reliability. To test the scale items Confirmatory Factor Analysis (CFA) was used. Present study reported Comparative Fit index (CFI), Root Mean Square Error of Approximation (RMSEA), Root Mean Residuals (RMR). The six latent constructs of the measurement model are tested to check if all the coefficients indicate FWFH. The coefficient values show that work dependence, work life balance and work environment are significant determinants of FWFH.

Extensive literature review has revealed the existing models developed by various researches. The Model framework proposed by Nordin et al., 2016 is as under. Previous research findings and the model framework set by Nordin et al., 2016 was studied. The change in the circumstances advocate the need for supplementary variables to the existing model. We would like to study the moderating effect of pandemic lockdown on employee preference to WFH post pandemic.

H1: There is a positive influence of job performance on employee’s willingness to FWFH

As it is identified by many researchers and evident from the previous literature that job performance is one of the essential components in the study of work from home. The authors Garg and van der Rijst ( 2015 ) have studied the relationship between the job performance and professional isolation. Job performance and work from home are related and are inter dependent. When there is clear understanding of job performance and when the job indicators are quantifiable, work from home possibility is more even after pandemic. Therefore, it is hypothesized as there is a positive influence of job performance on work from home in future.

H2: There is a negative influence of work dependence on employee’s willingness to FWFH

In past research was directed towards the importance of telecommuting and increasing work dependence (Vana et al., 2008 ). The study made by Garg and van der Rijst ( 2015 ) found that work dependence had a weak positive relation to experience with virtual work. The focus of present study is to assess the willingness of employees to work from home post pandemic. The present study is during the peculiar times of Covid 19 which makes the concept of WFH a unique one.

H3: There is a negative influence of social interaction on employee’s willingness to FWFH

Another important component of factors influencing willingness to work from home in future (FWFH) is Social Interaction. Previous studies (Baumeister & Leary, 1995 ) have highlighted that work from home with less social interaction in employees will make them aggravated due to isolation. Mintz-Binder & Allen, 2019 observed the factor social contact in terms of virtual meetings and online interactions. Many researchers in the past have focussed on the need to maintain firm and well-built interpersonal social relationships. There exists a negative influence of social interaction on work from home in near future.

H4: There is a positive influence of supervisor’s role on employee’s willingness to FWFH

Raghuram and Fang ( 2014 ) have studied the role of the supervisor in controlling the employees working from home. Previously Lautsch et al. ( 2009 ) have studied the general perceptions regarding supportiveness of supervisors. Madlock ( 2012 ) has studied the leadership styles and their results suggested that supervisors occupied in work oriented more than relational oriented leadership style in the virtual workplace.

H5: There is a negative influence of work environment on employee’s willingness to FWFH

According to Wheatley ( 2012 ), work from home eliminates the workplace related distractions and allows to work productively without interruptions. The results of the present study are in agreement with the study conducted by Golden ( 2007 ) which pointed out that the virtual technology like e-mail and online-conferences to interact with other employees lack the warmth and social presence of face-to-face interaction.

H6: There is a positive influence of work life balance on employee’s willingness to FWFH

Study conducted by Venkatraman et al. ( 1999 ) emphasised that working overtime informally without any extra payment affects the personal life of the employees. The study conducted by Tietze and Musson ( 2010 ) elicits that balance between work and home is essential to understand the relationship between household and professional life. The results of the present study agreed with a balanced work and family life will have greater willingness to work from home. Thus, the proposed hypothesis is that there is a positive influence of work life balance on the employee’s willingness to work from home in future (FWFH).

Demographic Profile of Respondents

The study consisted of 138 participants working from home during the lockdown. 21% of respondents were female whereas 79% were male. The largest group 58% fall in the age group of 18–25 years, 34% of respondents were in 26–35 years of age group and 36–45 years of the age group is represented by 8% in the current study. The largest group 50% are Professionals (None of them are front end medical workers), 24% are IT software employees and others represent 26% (Design engineers, BPO employees and backend support). In terms of the highest educational qualification, 45% of participants were degree/diploma holders, 40% were postgraduates and 16% were holding a professional qualification. None of them were below graduation level, the group is mature.

Data Screening

The responses were complete in all aspects. There is no missing data in the columns. Also, observed quite normally distributed data of our latent factors and other variables like job performance, work dependence, social interaction, supervisor’s role, work environment and work life balance. To measure the multivariate normality, kurtosis and skewness measures were used which was generated using AMOS 26. The data exhibited normal distribution which ranged from −1.3 to 2.04. The threshold value for Kurtosis and Skewness is −2 to +2 (Byrne, 2010 ). However, the value of 2.04 does not violate the normality. The threshold is 3.3 according to Skarpness, 1983 . This number indicates a good fit. Multivariate Analysis was suggested by Hu & Bentler, 1998 as an indication of goodness of fit. The multivariate measure in the study is 15.472 at critical ratio 1.298. The data is perfectly well behaved.

The present study has attempted to explore the structural relationship between the multiple factors relating to Work from Home. Questions were measuring the variables on five point Likert scale. This was run in SPSS 28 using Varimax with Normalization method for rotation. The rotation and iteration were run until the ultimate clear pattern matrix arrived. The factor patterns arrived under each column were thoroughly diagnosed to understand the plausible cross-loadings of factors and elimination of redundant variables (Brown & Moore, 2012 ). Six factors were identified under different heads like job performance (JP), work dependence (WD), work life balance (WLB), social interaction (SI), supervisor’s role (SR) and work environment (WE). These six factors explained were calculated from the sum of squared loadings from the structure matrix. The total accumulated variance explained is 71.709% for work from home during pandemic. The total variance explained by first factor job performance is 13.65%, the second factor work dependence is 13.656%, work life balance is 12.965, social interaction is 12.450, supervisor’s role is 10.392 and work environment is 8.998. Absolute values below 0.5 were eliminated. During the principal axis factoring, few items cross loaded on another component and few items in scale were deleted due to low factor loadings. An item in the job performance scale “There are objective criteria by which my performance can be evaluated” was cross loaded on supervisor’s role component during factor analysis. Third item in work life balance was deleted due to poor loading. The rotation converged in 7 iterations. Bartlett’s Test of Sphericity was significant at 000 indicating the result was acceptably valid. In addition to this, the model fit indices were verified for the proposed factor structure. The CFA result yielded an adequate fit. The CMIN = 164.268, CMIN/df = 1.711, CFI = 0.922, RMSEA = 0.08, RMR = 1.55 (See Appendix Table 3 ). The overall model exhibited a good fit. The Harman single factor test was used for examining if the problem of common method variance (CMV) exists or not. All the factors have not significantly loaded on a single factor. This test confirms that CMV is not a significant problem in this study.

The job performance scaled on three measures. It is easy to measure and quantify employee performance (with path coefficients = 0.932), the measures of employee job performance are clear (with path coefficients = 0.829), the feeling that employee engagement is more during the lockdown (with path coefficients = 0.704). The hypotheses that there exists a positive influence of job performance on employee’s willingness to WFH in future is refuted with estimate of 0.003 at p value greater than 0.05. There is a negative influence of work dependence on employee’s willingness to WFH in future. In this factor three aspects of work dependence are measured, the extent to which the employee performance depends on working with others (with path coefficients 0.892), the need to work independently for performing the best (with path coefficients 0.872), the nature of work in terms of independent task or projects (with path coefficients 0.675). All three are significant with p value less than 0.05. However, the study has revealed the negative influence of Work Dependence on employee’s willingness to work from home in future post pandemic situation. It may be inferred that the higher degree of WFH is associated with weakened work dependence. This is due to the inter-dependence of departments for work completion. Like for example, the dependence on IT department for setting up remote access to all the employees for completion of work during the sudden lockdown. Next, social interaction was measured. The first item, social interactions are more in the current lock down situation (deleted due to low loadings), The work-related meetings in my office are adequate to build good working relationships (with path coefficient 0.915), the social events in virtual office are adequate to build a sense of community (with path coefficient 0.725). The research hypotheses relating to negative influence of social interaction on employee’s willingness to WFH in future is refuted in the current study. The relationship between social interaction and willingness to WFH in future is −0.193 at p value greater than 0.05. Thus, we refute the hypothesis.

The results of the present study hypothesize that there is a positive influence of supervisor’s role on employee’s willingness to WFH in future has been refuted. In the present study focused on three aspects of supervisory role. The first being close supervision of work during the lockdown (with path coefficients 0.902). Secondly, employees understanding on the criteria for evaluating the performance was studied (with path coefficients 0.760). Lastly, the support extended by the superior in addressing problems during the lockdown (with path coefficients 0.673) was studied. The supervisor’s role estimated −0.002 at p value more than 0.05. Thus, hypothesis is rejected under study that there is a positive influence of supervisor’s role on employee willingness to WFH in future.

Hypothesis results have revealed that there is a significant negative influence of work environment on employee’s willingness to WFH in future (with path coefficients −0.245). In this factor, three aspects of work environment were measured, the interruption caused when colleagues talk in virtual meetings (with path coefficients 0.746) and the distraction caused by other things going on in the work environment, such as background noise (with path coefficients 0.802) and feeling of pressure because meetings take away from work (with path coefficients 0.632) are measured under this head. Moreover, it consumes lot of productive time to effecting work particularly for the complex type of tasks. It may be inferred that the higher degree of willingness to WFH is associated with weakened work environment.

Work life balance is measured using three items. Overall comfort working from home (with path coefficient 0.630), employee’s ability to balance both work and household during the lock down (with path coefficient 0.909) and feeling of difficulty in maintaining work life balance due to the pressure of remaining available all the time (deleted due to low loadings). There is a positive influence of work life balance on employees willingness to WFH in future with regression estimate of 0.546 at p value less than 0.05. It may be inferred that higher degree of work life balance has an incremental effect on willingness to WFH.

Assessment of Reflective Model

Reliability analysis.

Cronbach Alpha was used to assess the inter item consistency between measurement variables. Cronbach’s Alpha for all the factors put together was 0.708. Post factorization, the Cronbach’s Alpha for job performance was 0.750, work dependence was 0.844, work life balance was 0.75, social interaction was 0.64, superior’s role was 0.781 and work environment was 0.66. All these values are above 0.6 indicating acceptable internal consistency (Nunnally, 1978 ). Next, Composite Reliability (CR) was assessed. CR values ranged from 0.753 to 0.865 higher than minimum requirement of 0.7 (see Appendix Table  4 ).

Convergent Validity

Convergent validity was assessed using Average Variance Explained (AVE). The AVE values ranged from 0.533 to 0.684 higher than 0.5 threshold. The factor loadings exceeded 0.5 minimum requirement (Fornell & Larcker, 1981 ). Thus, Convergent Validity was assured.

Discriminant Validity

Discriminant validity is assured by comparing the square root of AVE and inter-correlations between other constructs as exhibited in Appendix Table  5 . The diagonal bold numbers in the table indicate square root of AVE and the non-diagonal numbers are the correlations between constructs signifying discriminant validity.

Content Validity

It is very important to take utmost care while designing the questionnaire. The questionnaire was simple in its structure and the language used was easy to understand. This was principally designed to get better content validity.

Structural Model Testing

Hypothesis testing.

In the structural model analysis, multi-dimensional model was hypothesised and tested for significance. While testing the objectives under the study, it was encountered that three out of six path coefficients were considered statistically significant. Work dependence (with path coefficients −0.345), work environment (with path coefficients −0.245), work life balance (with path coefficients 0.546) are significantly related to employee willingness to WFH in future post pandemic. While job performance, social interaction and supervisor’s role are not statistically significant (See Appendix Table  6 ).

As predicted in Hypothesis 2, work dependence is negatively associated with FWFH (β = −0.345, p < 0.05). Hypothesis 5, work environment is negatively associated with FWFH (β = −0.245, p < 0.05). Hypothesis 6, work life balance is negatively associated with FWFH (β = 0.546, p < 0.05). Hypothesis 2, 5 and 6 are supported.

Unexpectedly, Hypothesis 1 that states that there exists a positive influence of job performance on FWFH was not supported. Hypothesis 3, that there is a negative influence of social interaction on FWFH was also not supported. Finally, Hypothesis 4, that there is a positive influence of supervisor’s role on FWFH was also not statistically significant (See Fig. 1 )

Number of variables relating to work infrastructure at home, work dependence, virtual meetings, supervision, performance, social interactions with co-workers, challenges encountered and work life balance were measured in this study (See Fig. 2 ). Based on the availability of work related infrastructure at home during lock down, this part of the survey tries to access the willingness and the future possibility of WFH if required. 82% of respondents confirmed that they are ready to work from home if they are given an opportunity and if such situations demand in future. Moreover, 82% had confirmed that they have internet connection at home, 50% of total respondents confirmed that they have air-conditioning at home, 60% respondents confirmed that they have separate space to work from home, 79% of participants opined that their home office were silent. 87% had computer/laptop/headphones and other accessories required for WFH. This indicates that most of them have access to basic work related infrastructure. It also indicates the future possibility of work from home. 79% of respondents agree that they felt there is a close supervision of work during the lockdown out of which 29% of respondents strongly agreed. This indicates that the amount of supervision over their work has increased comparatively. 76% agreed that they felt that employee engagement is more during the lock down out of which 26% of them strongly agreed. None of them strongly disagreed that employee engagement is more during lockdown.

In perceived organizational support, the survey made an attempt to study the superior’s support towards the team members in addressing various work related problems during the remote working scenario. It has been observed that superiors strongly support their teams when they confront any problems relating to work. Majority of them 83% agreed that they have a very supportive work environment out of which 23% of participants strongly agreed. Moreover, 71% agreed that social interactions were must, whereas 7% denied its importance. However, 21% were neutral.

The social events in virtual offices needs to be adequate to build a sense of community and break the social isolation among the teams. 61% agreed that they had adequate social events with co-workers in virtual office whereas 29% of them were neutral and only 10% of participants complained of not having adequate social events.

With respect to the adequacy of work related meetings, 68% of the participants agreed that the work-related meetings in the virtual office were adequate. This indicates that most of the employees working from home are closely connected through work related meetings. This is a good indicator of building a work relationship even during the lockdown in-spite of physical isolation. Only 5% feel that there are not much adequate interactions in terms of work related team meets as before.

Team meetings are a great way to come together with the colleagues and clients both inside and outside of the organization. The online platforms which are being commonly used in Indian scenario are zoom, google meet, webex, microsoft teams, go to meeting, kaizala and skype other service providers which they agreed to be very effective tools for managing virtual teams. However, it is also observed that certain problems and challenges with respect to internet connectivity, server issues, call drops, hacking and data insecurity during the lockdown were encountered. The study found that 55% of respondents agreed that messaging and chat has improved the team effectiveness. This study has revealed the role of technology in building the virtual workspace. Another problem which has surfaced during the study is the fact that the pressure to be available online all the time has affected the work life balance. 63% of participants agreed that post pandemic also work from home permits should be given. Thus, 63% of employees are comfortable with work from home.

The evidence conferred suggests that WFH is on the whole beneficial to both organizations and its employees. Majority of the respondents agreed to WFH post pandemic with clarity on their performance indicators and enhanced productivity, it can be concluded that WFH during the pandemic is an overall WIN-WIN situation for the employees and the corporate (Garg & van der Rijst, 2015 ). However, home space has become the work area affecting the overall work life balance with long working hours, pressure to be available all the time. In conclusion, the tech problems associated with remote working due to unpreparedness with respect to COVID 19 cataclysm has contributed to the existing challenges of the employees and organizations. It has also been observed that remote working has built a pressure on the home networks which led to frequent interruption in the regular working. Moreover, hacking and data security threats have added to the existing problems. Poor network quality coupled up with frequent call drops, server and connectivity problems are few more issues noticed.

With this, it can be concluded that despite all these challenges faced by the employees the exemplary attitude of employees towards WFH is commendable. It has been observed that majority of respondents have agreed to WFH post lockdown which truly exhibits the spirit to cooperate and abide by the nations call towards adhering to the timely health guidelines without affecting the productivity.

The current seismic circumstances are directing organizations and its employees into a new era of WFH. Employee engagement and supervision coupled alongside supervisor’s support is the only way ahead. Catching up formally and informally through conference calls is the only mode to build teams effectiveness and team inclusion without compromising the productivity and the work enthusiasm is the new reality.

Implications of the Study

The change in the place of working calls for the attention of the labour laws. The Government needs to redefine the existing labour laws in the country. The traditional laws related to workplace requires to be replaced with the changing needs of WFH. This calls for framing of new HR policies in organisations in order to ensure perfect work life balance.

Limitations of Study and Scope for Further Research

Nevertheless, the present study has limitations. The study is limited to a small group of participants of private organizations including young educated working professionals, IT software employees, design engineers, BPO employees and backend support employees working from home. In this study, employees working in essential services and health care were excluded. The recommended future direction for research would be to study using a feasibly larger sample of survey and test the validity. The study is social desirability response bias. Although the anonymity was assured to the respondents there could be a possibility of bias in participation. Social desirability response bias in self report research as pointed out by authors Van de Mortel ( 2008 ) may have transpired. The present study calls for the attention of researchers towards WFH in educational sector and challenges of smart teaching and learning. The impact of WFH and professional isolation on physical and mental well-being should also be further investigated in order to develop preparedness of management during contingencies.

Data Availability

All data generated or analysed during this study are included in this published article.

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Tunk, N., Kumar, A.A. Work from home - A new virtual reality. Curr Psychol 42 , 30665–30677 (2023). https://doi.org/10.1007/s12144-021-02660-0

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How working from home works out

Key takeaways.

• Forty-two percent of U.S. workers are now working from home full time, accounting for more than two-thirds of economic activity.
• Policymakers should ensure that broadband service is expanded so more workers can do their jobs away from a traditional office.
• As companies consider relocating from densely populated urban centers in the wake of the COVID-19 crisis, cities may suffer while suburbs and rural areas benefit.
• Working from home is here to stay, but post-pandemic will be optimal at about two days a week.

Working from home (WFH) is dominating our lives. If you haven’t experienced the phenomenon directly, you’ve undoubtedly heard all about it, as U.S. media coverage of working from home jumped 12,000 percent since January 1 .

But the trend toward working from home is nothing new. In 2014 I published  a study  of a Chinese travel company, Ctrip, that looked at the benefits of its WFH policies (Bloom et al. 2014). And in the past several months as the coronavirus pandemic has forced millions of workers to set up home offices, I have been advising dozens of firms and analyzing four large surveys covering working from home. 2

The recent work has highlighted several recurring themes, each of which carries policy questions — either for businesses or public officials. But the bottom line is clear: Working from home will be very much a part of our post-COVID economy. So the sooner policymakers and business leaders think of the implications of a home-based workforce, the better our firms and communities will be positioned when the pandemic subsides.

The US economy is now a working-from-home economy

Figure 1 shows the work status of 2,500 Americans my colleagues Jose Barrero (ITAM) and Steve Davis (Chicago) and I surveyed between May 21-25. The responders were between 20 and 64, had worked full time in 2019, and earned more than $20,000. The participants were weighted to represent the U.S. by state, industry, and income.

We find that 42 percent of the U.S. labor force are now working from home full time, while another 33 percent are not working — a testament to the savage impact of the lockdown recession. The remaining 26 percent are working on their business’s premises, primarily as essential service workers. Almost twice as many employees are working from home as at a workplace.

If we weight these employees by their earnings in 2019 as an indicator of their contribution to the country’s GDP, we see that these at-home workers now account for more than two-thirds of economic activity. In a matter of weeks, we have transformed into a working-from-home economy.

Although the pandemic has battered the economy to a point where we likely won’t see a return to trend until 2022 (Baker et al. 2020), things would have been far worse without the ability to work from home. Remote working has allowed us to maintain social distancing in our fight against COVID-19. So, working from home is a not only economically essential, it is a critical weapon in combating the pandemic.

Figure 1: WFH now accounts for over 60% of US economic activity

Source:  Response to the question  “Currently (this week) what is your work status?”  Response options were  “Working on my business premises“ ,  “Working from home” ,  “Still employed and paid, but not working“ ,  “Unemployed, but expect to be recalled to my previous job“ ,  “Unemployed, and do not expect to be recalled to my previous job“ ,  and  “Not working, and not looking for work“

Data from a survey of 2,500 US residents aged 20 to 64, earning more than $20,000 per year in 2019 carried out between May 21-29, by QuestionPro on behalf of Stanford University. Sample reweighted to match current CPS.

Shares shown weighted by earnings and unweighted (share of workers)

The inequality time bomb

But it is important to understand the potential downsides of a WFH economy and take steps to mitigate them.

Figure 2 shows not everyone can work from home. Only 51 percent of our survey reported being able to WFH at an efficiency rate of 80 percent or more. These are mostly managers, professionals, and financial workers who can easily carry out their jobs on their computers by videoconference, phone, and email.

The remaining half of Americans don’t benefit from those technological workarounds — many employees in retail, health care, transportation, and business services cannot do their jobs anywhere other than a traditional workplace. They need to see customers or work with products or equipment. As such they face a nasty choice between enduring greater health risks by going to work or forgoing earnings and experience by staying at home.

Figure 2: Not all jobs can be carried out WFH

Source:  Data from a survey of 2,500 US residents aged 20 to 64, earning more than $20,000 per year in 2019 carried out between May 21-25 2020, by QuestionPro on behalf of Stanford University. Sample reweighted to match the Current Population Survey.

In Figure 3 we see that many Americans also lack the facilities to effectively work from home. Only 49 percent of responders can work privately in a room other than their bedroom. The figure displays another big challenge — online connectivity. Internet connectivity for video calls has to be 90 percent or greater, which only two-thirds of those surveyed reported having. The remaining third have such poor internet service that it prevents them effectively working from home.

Figure 3: WFH under COVID-19 is challenging for many employees

Source:   Pre-COVID data from the BLS ATUS . During COVID data from a survey of 2,500 US residents aged 20 to 64, earning more than $20,000 per year in 2019 carried out between May 21-25 2020, by QuestionPro on behalf of Stanford University. Sample reweighted to match the Current Population Survey.

In Figure 4, we see that more educated, higher-earning employees are far more likely to work from home. These employees continue to earn, develop skills, and advance careers. Those unable to work from home — either because of the nature of their jobs or because they lack suitable space or internet connections — are being left behind. They face bleak prospects if their skills erode during the shutdown.

Taken together, these findings point to a ticking inequality time bomb.

So as we move forward to restart the U.S. economy, investing in broadband expansion should be a major priority. During the last Great Depression, the U.S. government launched one of the great infrastructure projects in American history when it approved the Rural Electrification Act in 1936. Over the following 25 years, access to electricity by rural Americans increased from just 10 percent to nearly 100 percent. The long-term benefits included higher rates of growth in employment, population, income, and property values.

Today, as policymakers consider how to focus stimulus spending to revive growth, a significant increase in broadband spending is crucial to ensuring that all of the United States has a fair chance to bounce back from COVID-19.

Figure 4: WFH is much more common among educated higher-income employees

Source:  Pre-COVID data from the BLS ATUS . During COVID data from a survey of 2,500 US residents aged 20 to 64, earning more than $20,000 per year in 2019 carried out between May 21-25 2020, by QuestionPro on behalf of Stanford University. Sample reweighted to match the Current Population Survey. We code a respondent as working from home pre-COVID if they report working from home one day per week or more.

Trouble for the cities?

Understanding the lasting impacts of working from home in a post-COVID world requires taking a look back at the pre-pandemic work world. Back when people  went  to work, they typically commuted to offices in the center of cities. Our survey showed 58 percent of those who are now working from home had worked in a city before the coronavirus shutdown. And 61 percent of respondents said they worked in an office.

Since these employees also tend to be well paid, I estimate this could remove from city centers up to 50 percent of total daily spending in bars, restaurants, and shops. This is already having a depressing impact on the vitality of the downtowns of our major cities. And, as I argue below, this upsurge in working from home is largely here to stay. So I see a longer-run decline in city centers.

The largest American cities have seen incredible growth since the 1980s as younger, educated Americans have flocked into revitalized downtowns (Glaeser 2011). But it looks like 2020 will reverse that trend, with a flight of economic activity from city centers.

Of course, the upside is this will be a boom for suburbs and rural areas.

Working from home is here to stay

Working from home is a play in three parts, each totally different from the other. The first part is  pre -COVID. This was an era in which working from home was both rare and stigmatized.

A  survey of 10,000  salaried workers conducted by the Bureau of Labor Statistics showed only 15 percent of employees ever had a full day working from home. 3

Indeed, only 2 percent of workers ever worked from home full time. From talking to dozens of remote employees for my research projects over the years, I found these are mostly either lower-skilled data entry or tele-sales workers or higher-skilled employees who were able to do their jobs largely online and had often been able to keep a job despite locating to a new area.

Working from home before the pandemic was also hugely stigmatized — often mocked and ridiculed as “shirking from home” or “working remotely, remotely working.”

In a 2017  TEDx Talk , I showed the result from an online image search for the words “working from home” which pulled up hundreds of negative images of cartoons, semi-naked people or parents holding a laptop in one hand and a baby in the other.

Working from home  during the pandemic is very different. It is now extremely common, without the stigma, but under  challenging conditions . Many workers have kids at home with them. There’s a lack of quiet space, a lack of choice over having to work from home, and no option other than to do this full time. Having four kids myself I have definitely experienced this.

COVID has forced many of us to work from home under the worst circumstances.

But working from home  post- COVID should be what we look forward to. Of the dozens of firms I have talked to, the typical plan is that employees will work from home between one and three days a week and come into the office the rest of the time. This is supported by our evidence on about 1,000 firms from the  Survey of Business Uncertainty  I run with the Atlanta Fed and the University of Chicago. 4

Before COVID, 5 percent of working days were spent at home. During the pandemic, this increased eightfold to 40 percent a day. And post-pandemic, the number will likely drop to 20 percent.

But that 20 percent still represents a fourfold increase of the pre-COVID level, highlighting that working from home is here to stay. While few firms are planning to continue full time WFH after the pandemic ends, nearly every firm I have talked to about this has been positively surprised by how well it has worked.

The office will survive but it may look different

“Should we get rid of our office?” I get that question a lot.

The answer is “No. But you might want to move it.”

Although firms plan to reduce the time their employees spend at work, this will not reduce the demand for total office space given the need for social distancing. The firms I talk to are typically thinking about halving the density of offices, which is leading to an increase in the overall demand for office space. That is, the 15 percent drop in working days in the office is more than offset by the 50 percent increase in demand for space per employee.

What is happening, however, is offices are moving from skyscrapers to industrial parks. Another dominant theme of the last 40 years of American cities was the shift of office space into high-rise buildings in city centers. COVID is dramatically reversing this trend as high rises face two massive problems in a post-COVID world.

Just consider mass transit and elevators in a time of mandatory social distancing. How can you get several million workers in and out of major cities like New York, London, or Tokyo every day keeping everyone six feet apart? And think of the last elevator you were in. If we strictly enforce six feet of social distancing, the maximum capacity of elevators could fall by 90 percent 5 , making it impossible for employees working in a skyscraper to expediently reach their desks.

Of course, if social distancing disappears post-COVID, this may not matter. But given all the uncertainty, my prediction is that when a vaccine eventually comes out in a year or so, society will have become accustomed to social distancing. And given recent nearly missed pandemics like SARS, Ebola, MERS, and avian flu, many firms and employees may be preparing for another outbreak and another need for social distancing. So my guess is many firms will be reluctant to return to dense offices.

So what is the solution? Firms may be wise to turn their attention from downtown buildings to industrial park offices, or “campuses,” as hi-tech companies in Silicon Valley like to call them. These have the huge benefits of ample parking for all employees and spacious low-rise buildings that are accessible by stairs.

Two types of policies can be explored to address this challenge. First, towns and cities should be flexible on zoning, allowing struggling shopping malls, cinemas, gyms, and hotels to be converted into offices. These are almost all low-rise structures with ample parking, perfect for office development.

Second, we need to think more like economists by introducing airline-style pricing for mass transit and elevators. The challenges with social distancing arise during peak capacity, so we need to cut peak loads.

For public transportation this means steeply increasing peak-time fares and cutting off-peak fares to encourage riders to spread out through the day.

For elevator rides we need to think more radically. For example, office rents per square foot could be cut by 50 percent, but elevator use could be charged heavily during the morning and evening rush hours. Charging firms, say $10 per elevator ride between 8:45 a.m. and 9:15 a.m. and 4:45 p.m. and 5:15 p.m., would encourage firms to stagger their working days. This would move elevator traffic to off-peak periods with excess capacity. We are moving from a world where office space is in short supply to one where elevator space is in short supply, and commercial landlords should consider charging their clients accordingly.

Making a smooth transition

From all my conversations and research, I have three pieces of advice for anyone crafting WFH policies.

First, working from home should be part time.

Full-time working from home is problematic for three reasons: It is hard to be creative at a distance, it is hard to be inspired and motivated at home, and employee loyalty is strained without social interaction.

My experiment at Ctrip in China followed 250 employees working from home for four days a week for nine months and saw the challenges of isolation and loneliness this created.

For the first three months employees were happy — it was the euphoric honeymoon period. But by the time the experiment had run its full length, two-thirds of the employees requested to return to the office. They needed human company.

Currently, we are in a similar honeymoon phase of full-time WFH. But as with any relationship, things can get rocky and I see increasing numbers of firms and employees turning against this practice.

So the best advice is plan to work from home about 1 to 3 days a week. It’ll ease the stress of commuting, allow for employees to use their at-home days for quiet, thoughtful work, and let them use their in-office days for meetings and collaborations.

Second, working from home should be optional.

Figure 5 shows the choice of how many days per week our survey of 2,500 American workers preferred. While the median responder wants to work from home two days a week, there is a striking range of views. A full 20 percent of workers never want to do it while another 25 percent want to do it full time.

The remaining 55 percent all want some mix of office and home time. I saw similarly large variations in views in my China experiment, which often changed over time. Employees would try WFH and then discover after a few months it was too lonely or fell victim to one of the three enemies of the practice — the fridge, the bed, and the television — and would decide to return to the office.

So the simple advice is to let employees choose, within limits. Nobody should be forced to work from home full time, and nobody should be forced to work in the office full time. Choice is key — let employees pick their schedules and let them change as their views evolve. The two exceptions are new hires, for whom maybe one or two years full time in the office makes sense, and under-performers, who are the subject of my final tip.

Third, working from home is a privilege, not an entitlement.

For WFH to succeed, it is essential to have an effective performance review system. If you can evaluate employees based on output — what they accomplish — they can easily work from home. If they are effective and productive, great; if not, warn them, and if they continue to underperform, haul them back to the office.

This of course requires effective performance management. In firms that do not have effective employee appraisal systems management, I would caution against working from home. This was the lesson of  Yahoo in 2013 . When Marissa Mayer took over, she found there was an ineffective employee evaluation system and working from home was hard to manage. So WFH was paused while Mayer revamped Yahoo’s employee performance evaluation.

The COVID pandemic has challenged and changed our relationships with work and how many of us do our jobs. There’s no real going back, and that means policymakers and business leaders need to plan and prepare so workers and firms are not sidelined by otherwise avoidable problems. With a thoughtful approach to a post-pandemic world, working from home can be a change for good.

Figure 5: There is wide variation in employee demand for WFH post-COVID

Source:  Response to the questions: “In 2021+ (after COVID) how often would you like to have paid work days at home?“

Data from a survey of 2,500 US residents aged 20 to 64, earning more than $20,000 per year in 2019 carried out between May 21-25, by QuestionPro on behalf of Stanford University. 

Sample reweighted to match the Current Population Survey. 

1 Newsbank Access World News collection of approximately 2,000 national and local daily U.S. newspapers showing the percentage of articles mentioning “working from home” or “WFH.”

2 These are the  U.S. Bureau of Labor Statistics American Time Use Survey ; the  Survey of Business Uncertainty ; the  Bank of England Decision Maker Panel ; and the survey I conducted of 2,500 U.S. employees.

3   U.S. Bureau of Labor Statistics, Job Flexibilities and Work Schedules News Release. Sept. 24, 2019 .

4   Firms Expect Working from Home to Triple.  May 28, 2020. Federal Reserve Bank of Atlanta .

5  In a packed elevator each person requires about four square feet. With six-foot spacing we need a circle of radius six-feet around each person, which is over 100 square feet. If an elevator is large enough to fit more than one person, experts have advised riders to stand in your corner, face the walls and carry toothpicks (for pushing the buttons), as explained in this  NPR report .

Baker, S.R., Bloom, N., Davis, S.J., Terry, S.J. (2020). COVID-Induced Economic Uncertainty (No. 26983). National Bureau of Economic Research.

Bloom, N., Liang, J., Roberts, J., Zhichun, J.Y. (2014). Does Working from Home Work? Evidence from a Chinese Experiment. Quarterly Journal of Economics.

Glaeser, E. (2011). Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier and Happier. Penguin Books.

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What to Consider

Paper capacity.

Consider how much and how often you plan to shred. The paper capacity lists the number of sheets (though not exact) your paper shredder can handle at once. Small paper shredders have a capacity of 8 to 10, though if your shredding needs are few and far between, you can get away with a 6-sheet model. Mid-size paper shredders have a 25-sheet capacity and the largest we recommend, the Aurora, auto-feeds 10 sheets at once from a 120-sheet auto-feed tray. It’s great if you want to set up a big shredding project and go get other work done.

Manufacturers advise you not to shred cardboard, However, a 10-sheet paper has the ability to chomp the occasional cardboard, and our Best Overall pick Amazon Basics is known to efficiently shred a large amount of cardboard.

Paper shredders have different cut types, with micro and crosscut being the most common. They’re graded on seven levels of security (noted as P-1 to P-7) according to the international standard for secure shredding of data media developed by the German Institute for Standardization (DIN). Crosscut paper shredders have ratings pf P-3 and P-4, while micro-cut paper shredders can have ratings of P-4 and higher. For a visual, crosscut pieces are about the size of a dime and micro-cut pieces are like confetti.

Most of our options have a P-4 rating because they’re time-efficient, relatively inexpensive, and offer plenty of security. We do note the models that have a P-5 rating, but most of these machines are more expensive and a bit overkill for what most people need. Anything P-3-rated or under isn’t worth it.

Paper shredders collect pieces in a waste bin as your shred. Some require you to take the shredder off to empty the bin, which isn’t the most convenient. Pull-out waste bins are preferable because they’re easier to empty and safer; you don’t have to handle the shredder and put your hands near the blades.

To avoid a mess, we recommend putting a liner around the waste bin so you can tie it off and throw it away without any pieces escaping. For occasional shredding, a 3- to 5-gallon bin is plenty, but larger ones for a home office should have a 7- or 8-gallon capacity.

Runtime and Cooldown

Paper shredders can run anywhere from 20 to 40 minutes before needing to cool down, though smaller budget machines may only run for up to five minutes or so. The cooldown time is typically greater than the paper shredder’s runtime to ensure the machine is no longer hot. If you ignore the advised runtime, your paper shredder may overheat.

Extra Features

Some higher-end models have LEDs on the control panel to alert you when the waste bin is full, if the shredder is about to overheat, and if it’s overloaded. They can also help you troubleshoot issues like a paper jam or if the shredder isn’t properly attached to the waste bin. Most larger paper shredders that weigh upwards of 20 pounds have rolling casters. There are also auto-feed paper shredders, which are ideal for big jobs.

How We Tested

We’ve been using paper shredders for years–in home and office settings–for both security and basic paper disposal purposes. We kept this experience in mind when selecting the models for this list, and also performed extensive online research. We called in two popular models from Amazon Basics to test and see how they performed and measured the shredded pieces to see if they delivered on their claimed P-4 ratings. For models we didn’t test, we looked to trusted brands and vetted specs t0 ensure the shredders met our standards for quality and value. Since customers have a wide range of jobs and projects in mind when choosing a paper shredder, we made sure to include a variety of sizes, as well as a range of prices.

Amazon Basics 24-Sheet Crosscut Paper Shredder

Admittedly, this Amazon paper shredder impressed us more than we expected. It shredded a stack of 28 papers and worked the entire runtime promised by the product description—a full 40 minutes without overheating.

It’s a crosscut shredder, but the resulting pieces are considerably smaller than other similar models. It cut 24 sheets down to 4 by 30-millimeter pieces in just under 14 seconds. The output is relatively quiet as we measured its noise level at 70 decibels—about the same as a standard washing machine or dishwasher.

We didn’t shred cardboard during testing and recommend you stick to what the manufacturer instructions say. B ut it’s worth noting this exact model is touted as a “cardboard shredding beast” on Reddit and is great for composting.

It’s likely overkill for the average home office, and it doesn’t come cheap. But it offers solid performance all around. The only caveat is that the pull-out bin is a bit awkward and messy to clean up.

Amazon Basics 8-Sheet Paper Shredder

This shredder looks similar to our top pick but didn’t impress us nearly as much. That said, at such a low price point, our complaints are admissible. It’s solid enough for those who want a machine that can shred the occasional batch of papers and won’t take up room under your desk. It doesn’t have wheels, but, it being only 8 pounds, we didn’t really miss them when moving it around.

The resulting pieces were closer to what you expect from crosscut shredders. It jammed up on us a couple of times but was easy to clear and continue shredding. We were able to shred seven sheets continuously but not the claimed eight.

Still, it achieved shredding at the rate of one sheet per second and is only slightly louder than the 24-sheet model above. It’s good for handling light jobs around your home and office.

Bonsen 10-Sheet Paper Shredder

This Bonsen has a relatively small capacity but features that rival higher-end models. It shreds paper, credit cards, and staples into micro confetti-sized pieces to ensure documents are beyond resurrection and boasts a P-5 rating.

It has a simple control panel with just three buttons: power, forward, reverse. It also has LED indicators to alert you if it’s overheating, it’s overloaded, the bin is full, or the bin is out. There’s a learning curve in figuring which issue each indicator is referencing, but it’s a user-friendly experience overall.

The bin pulls out and fills a bit less slowly since the confetti pieces take up less room. There’s also a window on the front to give you an idea of when to empty it.

Fellowes LX22M Paper Shredder

This Fellowes model has a sleek design and the impressive performance to match. It micro-shreds 20 sheets into tiny particles with ease offering more security than a crosscut shredder.

Its 8-gallon bin holds up to 750 shredded sheets of paper before it needs to be emptied and pulls out from the side—preferable over those that require you to lift the shredder off the bin and empty from the top. It also has a safety feature that pauses if it senses hands are too close to the blades and automatically resumes when the coast is clear.

LED lights notify when the bin is almost full and when it’s about to finish its runtime. It’s one of the quieter options out there, with a noise level of about 50 decibels, comparable to the hum of a refrigerator. It can’t shred CDs but can handle paper clips, staples, and credit cards.

Aurora High Security AU1000MA Paper Shredder

The micro-cut shredding capabilities of this model, combined with the power to rend CDs and credit cards, make it a great option if you prioritize maximum security. It can shred continuously for 12 minutes, though it requires a 40-minute cooldown time if it overheats. However, it automatically powers off after five minutes, saving energy and thus reducing the chance of damage.

The pull-out waste bin has a window to check when it’s nearly full, but the shredder also has LED notifications that alert you for things like when there’s a paper jam.

We’re also big fans of the five-year warranty on the cutting cylinders, and the one-year plan for the rest of the components.

Aurora 120-Sheet Paper Shredder

If you have a lot of shredding to do, this Aurora shredder is your best bet thanks to its convenient auto-feeder. The feeder tray also has a 120-sheet capacity, which is considerable.

This shredder creates compact micro-cut pieces for maximum security, and it can handle paper clips and staples. It can shred for up to 30 minutes at a time before needing a break. The waste bin has a convenient pull-out design for easy emptying.

Bonsaii 6-Sheet Paper Shredder

This low-budget shredder is powerful enough to handle six sheets at once, can process 36 sheets per minute, and has the strength to chew through staples and credit cards. Plus, there’s an overheat warning indicator to let you know when it’s time to give it a rest.

The large window on the bin allows you to see when it’s ready to empty, and the top-mounted handle makes it convenient to do so.

How to Maintain a Paper Shredder

Extend the life of your paper shredder with some best practices and operational tips.

• Oil the blades of the paper shredder to condition them and reduce friction—this helps with paper jams as well. Lubricant sheets make it even easier to oil the blades—simply feed them through the shredder as you would with regular paper.
• Don’t overload your paper shredder with more sheets than the advised paper capacity. This’ll reduce overall wear and tear and also help prevent paper jams.
• Don’t use your paper shredder to its maximum runtime. If you have a big shred job, take breaks every 15 minutes and turn the machine off to avoid waiting for a complete cooldown.
• While some paper shredders can handle staples and paper clips, none are particularly good at shredding adhesives or stickers. These can leave residue on the blades and cause buildup that leads to jams.

Danny Perez is a Commerce Editor for Popular Mechanics with a focus on men's style, gear, and home goods. Recently, he was coordinator of partnership content at another product journalism outlet. Prior to that, he was a buyer for an independent men's shop in Houston, Texas, where he learned all about what makes great products great. He enjoys thrifting for 90s Broadway tees and vintage pajama sets. His spare time is occupied by watching movies and running to impress strangers on Strava.

Alex Rennie is a freelance writer who specializes in the Home Improvement, DIY, and Tool space. As a former residential and commercial carpenter, Alex uses his hands-on experience to write practical buying guides, how-to articles, and product reviews. His work has also appeared in Business Insider's Insider Picks, and before his writing career, he was a full-time carpenter living in New York City. There, he worked as part of a team designing, building, and installing large furniture pieces, as well as performing a variety of home repair and maintenance projects. Alex currently lives in Los Angeles, CA, and spends his free time exploring the beaches and mountains with his fiancé and their dog Louie.

Matt Crisara is a native Austinite who has an unbridled passion for cars and motorsports, both foreign and domestic. He was previously a contributing writer for Motor1 following internships at Circuit Of The Americas F1 Track and Speed City, an Austin radio broadcaster focused on the world of motor racing. He earned a bachelor’s degree from the University of Arizona School of Journalism, where he raced mountain bikes with the University Club Team. When he isn’t working, he enjoys sim-racing, FPV drones, and the great outdoors.

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Clinical Trials Need Better Diversity: HHS Cites Work by CHIBE Affiliate

The Big Takeaway:

Dr. Scott Halpern (a member of CHIBE’s Internal Advisory Board) and colleagues wrote a paper on why diverse clinical trial participation matters , and they articulated several goals, which were cited by the Department of Health & Human Services (HHS) in a recent brief detailing its plans to increase diversity in clinical research.

The Problem:

A lack of diversity in clinical trials can lead to medical mistrust among marginalized communities, issues of fairness, and potentially stagnated biomedical knowledge, the authors of this New England Journal of Medicine paper wrote. Better trial representativeness could “improve the generalizability of research findings, produce new biologic insights, and yield targeted therapeutic strategies,” they stated.

The Proposal:

The authors argued that efforts to improve diversity cannot be sustained if the objectives aren’t clearly articulated. The paper pinpointed 3 main goals:

• Earning and building trust
• Promoting fairness
• Generating biomedical knowledge

The authors also offered several ways to reduce barriers for potential participants including:

• Providing transportation or parking vouchers
• Offering compensation or financial incentives
• Using mobile recruitment strategies
• Building inclusive trial infrastructure in underserved areas
• Streamlining the consent process
• Reducing the use of exclusion criteria

The Impact:

The Office of Science and Data Policy at the HHS published this brief on actions that HHS is taking to enhance diversity in clinical research. The brief specifically names the 3 goals the authors highlighted as key efforts to improve diversity and representativeness in clinical trials.

Additionally, Dr. Halpern was one of approximately 70 guests invited to the White House on June 26, 2024, for its Clinical Trials Forum to discuss efforts to improve the diversity of participants in clinical trials.

“It is wonderful to see our work being recognized by the White House’s Office of Science and Technology Policy and the Department of Health and Human Services,” Dr. Halpern said. “I believe we’re having an impact on national efforts to tackle the important problem of poor representativeness in clinical trial participation, but there is much work to be done above and beyond policy statements and executive meetings.”

Learn More:

Read the New England Journal of Medicine paper “ Why Diverse Clinical Trial Participation Matters ” written by Aaron L. Schwartz, MD, PhD; Marcella Alsan, MD, PhD; Alanna A. Morris, MD, and Scott D. Halpern, MD, PhD.

CHIBE Experts

• Scott Halpern, MD, PhD, MBE

Research Areas

• Health Equity

You might also be interested in...

CHIBE and Humana Discuss Changing Care Models in the 21st Century

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Research with integrity – GenAi, paper mills and inclusivity

27 June 2024

As the global research integrity community came together for the 8 th World Conference on Research Integrity, we asked: what are the big issues and what can we do to tackle them? Andrew Porter and the team give us the low-down from the front line of research integrity…

Climbing one of the hills near the Acropolis on the first evening of the 8 th World Conference on Research Integrity (WCRI) , I was treated to a wonderful view of Athens. Having taken the Metro from the airport, I’d only seen Athens at street level, but here it was… the whole city spread out.

This fresh perspective – seeing the big picture – made me think about the opportunities a conference like this offers to do the same. The space it can afford to think about what makes research tick, the different roles and activities supporting good research, and some of the bad practices affecting the quality of science.

The field of metascience, or research on research, is rapidly growing, evidenced by many presentations at this year’s conference about how to measure research integrity.

It may strike you as unusual that there are conferences on research integrity, but over the years it has become an active area of research in its own right. The field of metascience, or research on research, is rapidly growing, evidenced by many presentations at this year’s conference about how to measure research integrity. How can we know if a new training actually works? What data do we have on equality and diversity that could help us understand whose voices are not being heard in research, academia and publishing? How many publications contain fabricated, falsified or plagiarised data?

These and many other questions were discussed over 4 days in sessions ranging from the philosophical to the highly specific and practical.

The conferences began as a joint U.S. – European venture , expanding from a relatively small meeting in 2007 in Lisbon to this gathering of 800 attendees.  Along the way the conferences have produced various statements – co-created by attendees – which have helped shape research practices worldwide.  These include the 2010 Singapore Statement , which set out a values-based definition of research integrity, and forms the basis for the UK Concordat to Support Research Integrity and the recent Cape Town Statement on fairness, equity and diversity in research. For more on the WCRI, see Catherine’s earlier blog .

Intelligent approaches to Gen AI

Some of the big areas covered at the conference included paper mills and fake clinical trials ; the implications of generative AI; and equity, diversity and inclusion.  It’s clear, however, these aspects are deeply interwoven.

For instance, generative AI tools (many of which are now embedded in commercial software such as Photoshop 24 ) can be used to create fake text and data for paper mills, but might also help screen for fraudulent activity, support researchers writing in English as a second language, and require good training and education for ethical use.

Much of the conversation around gen AI is focussed on creating guidelines that are flexible and values-based; the field is moving so fast that making them too specific risks guidance going out of date. Addressing gen AI through the lens of existing research integrity structures however makes for more generalisable support, as discussed previously .

Fake it ‘till you break it…

Paper mills were a recurring theme across many different topic areas. I came away with a strong sense that we need to raise awareness amongst researchers – there is a real risk that fake research is polluting the literature.

Paper mills produce fake research publications for profit. Whole networks exist purely to sell authorship online, creating fake data and text, using fake email addresses and creating fake academics – even taking over the whole editorial and peer review process to completely bypass scrutiny. It’s a shocking concept, but   evidence of the scale of the problem keeps accumulating. For instance, over 8000 papers were retracted last year from Hindawi journals , a subsidiary of Wiley, primarily due to paper mill activity.

The pollution of scientific literature by fake studies leads to miscalculation of the size and importance of whole fields of research, meaning we can come to wrong conclusions on safety and efficacy

However, the research integrity community is stepping up to counter this. Sleuths, like Elisabeth Bik and Jana Christopher , work to detect these activities, along with academics developing tools to screen publications, such as Jack Wilkinson from the University of Manchester who presented the INSPECT-SR tool aimed at weeding out fake clinical trials.

Research integrity teams at publishers try to verify authorship, screen papers , obtain raw data, and use tools like iThenticate to spot plagiarism and image alteration . However, those wily paper mills will often submit manuscripts to multiple journals at the same time, and so the Committee on Publication Ethics (COPE) and STM , the trade body for academic publishers, are working on ways for publishers to spot these multiple submissions .

But this is, clearly, not just harmless cat-and-mouse antics. The very real dangers of this fraudulent activity were brought home in a number of sessions. One speaker reported raising concerns about more than 900 articles related to studies about women’s health, and while this has led to 151 retractions and 75 expressions of concern, a large volume of problematic literature persists – and it can take an average of 3 years for journals to address concerns.

The pollution of scientific literature by fake studies leads to miscalculation of the size and importance of whole fields of research, meaning we can come to wrong conclusions on safety and efficacy, even from systematic reviews (the gold standard for evidence-based medicine, influencing medical practice and government decision-making processes), and drive researchers down wasteful and frustrating dead ends.

Solution suggestions

How to address these problems? Suggestions included more people screening for issues already in the literature (like Jennifer Byrne whose team recently identified fake cell lines entering the literature), more research on the scale of the problem ( such as from the new voluntary body United2Act ), awareness raising for researchers and editors, and pre-screening manuscripts and verifying authenticity of data at an institutional level.

Attendees also advocated for deeper reform of academic publishing and reward models, for slower science (and the publication of fewer papers) and to move away from researchers being judged primarily on the number and type of papers they publish – something many institutions and funders have signed up to, but which still persists in research culture.

Some suggestions to address fraud – such requiring authors to have academic email addresses – could have unintended consequences, as researchers in low- and middle-income countries are often not provided with these. This reflected the ‘world’ part of the conference; funds are provided to support attendance from low- and middle-income countries, and it was encouraging to hear a diverse range of perspectives. A strong case was made that bringing in under-represented voices, making research truly global, representative and fair, is important for all those involved in research.

If we were to zoom out of the specific details of the conference, and try to get an overview, it might look something like: Bring in, Build up, and Keep out.

Bring in diverse and previously excluded voices. Build up good research practices, including for those researchers who are trying their best to act with integrity. Keep out fraudulent research, disinformation and fake data.

This framing might help us determine which kinds of initiatives, driven by which parts of the research community, are most effective and impactful for supporting integrity. WCRI has shown us much of what we have to do, and we have a decent map of the routes. The next step is to get back down to street level and implement some of them.

Take-home thoughts

Several CRUK research professionals attended WCRI – here’s what they think you should know…

Catherine Winchester, Head of the Research Integrity Service at the CRUK Scotland Institute

“It was clear that whatever role we have in a research organisation, we all have a part to play in collectively improving research quality and reproducibility, and that research integrity advisers are key partners in this endeavour. “Plan – do – act – check”, the take home message from Anja Gilis, director of preclinical quality planning and strategy at Johnson & Johnson, struck a chord with me and epitomises the iterative approach we have been implementing at the CRUK Scotland Institute.

One ‘doing’ initiative I learned about at the conference is the RoSiE project to foster reproducible open science in Europe, which is developing guidelines and training materials on open and FAIR science. And falling under ‘checking’, benchmarking surveys on culture, research integrity barriers and incentives were a popular theme at the conference. Looking forward it will be interesting to see how their information is used to act to change behaviour and practices. Indeed, the UK Committee on Research Integrity has undertaken a project to explore indicators of research integrity , which was presented by Jane Alfred.”

Sue Russell, Senior Policy and Governance Manager at CRUK

“It always pays to see what resources already exist to help you achieve your integrity goals. For example, we learned about SOPs4RI which helps research organisations and funders develop their own Research Integrity Promotion Plans.

How funders can then translate these into adaptive funding policies and help embed into broader practices will be important. Noesk’s Strategy for Culture Change – shared more than once at WCRI – was useful reminder of the foundations and levels needed.

But it will take collective, cross-sector collaboration – researchers, research integrity teams, research organisations, publishers, sector bodies, and funders coming together – to discuss and resolve issues. Great examples of fruitful collaborations were showcased at the conference from Only Good Antibodies to improve biomedical research and CRUK’s own Registered Reports Funding Partnership pilot – our consortium between us a funder, research organisation and publishers working together to improve research quality.

Our Research Integrity Advisors at CRUK Institutes have built very strong foundations – both within their institutions and more broadly – on this culture change journey, which WCRI made me appreciate even more.”

All sessions of the 8 th World Conference on Research Integrity were recorded, and recordings will be made publicly available 2 months after the conference.

Check out some of the posters presented by the CRUK integrity team at the 8th World Conference on Research Integrity

Dr andrew porter.

Andrew is Research Integrity and Training Adviser at Cancer Research UK Manchester Institute

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Working from home vs working from office in terms of job performance during the COVID‐19 pandemic crisis: evidence from China

Jingjing qu.

1 Shanghai AI Lab, China

2 School of Business and Administration, Northeastern University, Shenyang China

Despite being a worldwide disaster, the COVID‐19 pandemic has also provided an opportunity for renewed discussion about the way we work. By contextualizing in the early periods of China's ending of lockdown policy on COVID‐19, this paper offers evidence to respond to an essential discussion in the field of working from home (WFH): In terms of job performance, can WFH replace working from the office (WFO)? The present study compares job performance in terms of quality and productivity between WFH and WFO from 861 Chinese respondents using entropy balance matching, a quasi‐experimental methodology. Results reveal that WFH enhances job performance in terms of job quality but lowers it in terms of job productivity. In addition, the present study aims to capture and empirically measure the variations in fundamental job characteristics in terms of job control and job demand between WFH and WFO by applying the job demand control support model. More specifically, we find that job control items, such as ‘talking right’ and ‘work rate’, and job demand items, such as ‘a long time of intense concentration’ and ‘hecticness of the job’, are vital factors that contribute to how these differences exert influence on employees' performance in the context of the pandemic.

• WFH is positively related to job quality but negatively related to job productivity.
• WFH affects job performance via job demand and job control.
• Social support contributes to job productivity when working from home.

Introduction

The outbreak of the COVID‐19 pandemic boosted an unprecedentedly massive and rapid shift of people's work routines (Bartram and Cooke  2022 ; Yan et al.  2021 ). To a large extent, millions of employees around the world have been forced to resort to remote work (Bouziri et al.  2020 ; Hurley and Popescu  2021 ; Rogers  2021 ; Woods and Miklencicova  2021 ), which leads to the most significant social experiment of ‘working from home (WFH)’ emerging in human history (Zhang, Yu and Marin  2021 ). According to a report in LinkedIn, as Asia‐Pacific responded to the crisis, organizations in China, Australia, India and Singapore, quickly adapted to support a remote workforce. WFH differs considerably from working from office (WFO) in terms of job attributes and work environment. WFO is characterized by a relatively high degree of formalization and a fixed working routine, including place, time, and task arrangements (Palumbo  2020 ). Information and communications technology (ICT) was widely adopted with regard to work and organizational management (Balica  2019 ; Kassick  2019 ; Nemțeanu, Dabija and Stanca  2021 ; Olsen  2019 ). WFH is characterized by the freedom from constraints associated with working in a formal and fixed workplace due to progress in ICT (Nakrošienė, Bučiūnienė and Goštautaitė  2019 ).

Long before the COVID‐19 pandemic, WFH had already been suggested as a modern human resource policy for organizations, and it has resulted in a definite trend firmly entrenched in society (Illegems, Verbeke and S’Jegers  2001 ; Stanek and Mokhtarian  1998 ). It enables employees to be more productive by avoiding long commutes, skirting office politics, having fewer office distractions, and giving more chance to develop a better work–life balance (Hopkins and McKay  2019 ; Nakrošienė, Bučiūnienė and Goštautaitė  2019 ). Simultaneously, a stream of scholars have argued that WFH is not an alternative working routine and may even lead to poor employee performance (Fonner and Roloff  2010 ). Thus, a key question in the field has been raised: Can WFH replace WFO? Around this question, the debate has become fierce alongside the development of ICT and globalization. Nevertheless, past research has not yet reached a consensus, which constitutes a significant gap in the current knowledge.

Thus, drawing on the above research gaps, the present research is designed as a comparison study contextualized in the ongoing COVID‐19 pandemic. On the basis of the job demand–control–support (JDCS) model, a well‐documented theory that elucidates the effects of fundamental job characteristics (Johnson and Hall  1988 ), and combined with entropy balance matching (Watson and Elliot  2016 ), the present study investigates the difference between WFH group and other working cohorts in terms of job characteristics and its effects on job performance. More specifically, based on the JDCS model, we propose the mediation effect of job demand and job control and the moderation effect of employers' anti‐epidemic policy as the social support on the relationship between job demand/job control and employee job performance.

The contributions of this study are as follows. First, we shed new light on the mixed effects of WFH on job performance. We find that WFH can increase job quality but reduce job productivity. Second, underpinned by the JDCS framework, the present paper empirically tests the differences of job characteristics between WFH and other working routines regarding job demand, job control and social supports, and its direct and indirect effects on employees' satisfaction on performance. In this case, the present paper extends the JDCS model from the field of classical work routine to understand WFH. Furthermore, we employ the entropy balancing method to alleviate the methodological concerns with selection bias in the previous literature. Doing so allows for examining the causal effect of WFH on job characteristics and job performance to support the random hypothesis in comparison quasi‐experiment research.

The remainder of this paper is organized as follows. The next section presents the literature review, followed by a discussion of the hypothesis development. Further sections present the methods and results, respectively. The final section presents a discussion and implications, followed by future scope and conclusion.

Literature review

WFH is a working arrangement in which employees fulfill the essential responsibilities that their job entails while remaining at home using ICT (International Labor Organization  2020 , 5). Although a slight difference exists among terms such as WFH, teleworking, telecommuting and remote working, these concepts are largely interchangeable. WFH is considered home‐based teleworking, because teleworking may include various locations away from the primary worksite or the employers' premises (such as mobile working). Telecommuting refers to substituting telecommunications for commuter travel. Some differences exist between the terms teleworking and telecommuting, mainly because teleworking is broader and may not always be a substitute for commuting, but they are relatively minor. The basic difference between telework and remote work is that a teleworker uses personal electronic devices in addition to working physically remotely from a place other than an office or company premises, whereas remote work does not require visits to the main workplace or the use of electronic personal devices; and compared with WFH, remote work has the flexibility to work anywhere rather than being limited to the home. In addition, WFH may imply a long‐term contract, and individuals may have an emotional relationship with the organization; however, in remote work, this is not easy to achieve (Tønnessena, Dhira and Flåten  2021 ).

This paper aims to illustrate whether WFH can replace the classical working routine. A comparison study between WFH and other working routines seems to be a promising way to solve this question. However, we should consider two significant challenges of conducting a comparison study on WFH and other working routines. First, a ubiquitous theoretical framework is critical for providing solid support to capture fundamental job characteristics of diverse working routines. Only by doing so can we compare the difference between WFH and the other cohorts at the datum line. Second, we need to conquer the self‐selection bias. Most employees considering the possibility of WFH as the alternative way are familiar with applying ICT applications (e.g. email and online meeting apps) and necessary equipment (e.g. laptop and smartphone). In addition, employees' meta‐cognitive knowledge – their understanding of their capacity to cope with various situations under WFH ways (e.g. interruption caused by children and communication with line manager) – may play a similar self‐selective role. On the basis of these self‐selective factors, individuals evaluate the advantages and disadvantages of WFH and make decisions (Williams, McDonald and Cathcart  2017 ). Not controlling for this nonrandom self‐selection implies that observed job performance may reflect individuals' superior knowledge, capacity, or equipment rather than the actual effect of WFH. However, it is difficult to isolate the effects of job characteristics of WFH and the influence of individual heterogeneity explicitly associated with WFH. Thus, this paper adopts the JDCS model to investigate the effect of WFH on employees' job performance.

In the last 20 years, inconsistent findings have been found on the effect of WFH on employees' performance, especially in terms of work efficiency, turnover intention, goal completion, work motivation and job satisfaction (Gajendran and Harrison 2007 ; Golden  2006 ). On the one hand, some studies have found that WFH leads to high job performance (Bloom et al.  2015 ; Campo, Avolio and Carlier  2021 ; Choukir et al.  2022 ; Ipsen et al.  2021 ; Liu, Wan and Fan  2021 ). On the other hand, studies have found that WFH may lead to employees' lack of supervision, miscommunication, and less organizational commitment (Madell  2021 ). These disadvantages can create uncertainty that affects job satisfaction and consequently lead to lowering performance among employees, as gauged by companies' key performance indicators (Pepitone  2013 ). Some scholars have argued that WFH is negatively related to employees' job performance (Mustajab et al.  2020 ; Van Der Lippe and Lippényi  2020 ). Raišienė et al. ( 2020 ) suggested an investigation of the influence of WFH on job performance based on a contingency view, which depends on employees' gender, age, education, work experience, and telework experience. Table  1 summarizes the related literature.

Summary of related literature

Hypothesis development

Which one is better influence of wfh on job performance.

The JDCS model provides a sound theoretical basis for the influence of WFH on job performance. It originated from the job demand–control (JDC) model, which explains how job characteristics alter employees' stress, performance and satisfaction (Karasek and Theorell  1990 ). The JDC model posits two fundamental characteristics of an occupation: job demand and job control. Job demand is defined initially as ‘physical consumptions and psychological tensions involved in accomplishing the workload’, which negatively relate to workplace well‐being and relevant performance (Karasek and Theorell  1990 , 291). Job control (originally decision latitude) is the extent to which an employee has the authority to decide and utilize skills concerning the job and exert a positive effect on workplace outcomes. The JDCS model compounds the prominence of environmental factors on the overall well‐being within the workplace (Baka  2020 ). Thus, social support was integrated into the JDC model (named JDCS model) as a further fundamental characteristic of the work environment, implicating its synergistic effect on reducing stress and promoting well‐being in the working environment (Johnson and Hall  1988 ).

Given the inconsistent findings on the relationship between WFH and job performance, we further investigate the effect of WFH on job performance based on the JDCS model. The COVID‐19 pandemic has made WFH a sudden reality, as the ILO defined WFH in the context of the COVID‐19 pandemic as a temporary and alternative home‐based teleworking arrangement (ILO  2020 ). Waizenegger et al. ( 2020 ) articulated the differences between remote e‐working before and during the COVID‐19 pandemic.

Given the two mechanisms of JDCS, we further investigate the effect of WFC on job performance separately from the perspective of job demand and job control. On the one hand, WFH may lead to high job control, which benefits job performance, because not all job functions and tasks can be done outside the employers' premises or the specified workplace (Waizenegger et al.  2020 ). WFH is not practical or feasible or cannot be deployed quickly in some jobs and tasks (Williams, McDonald and Cathcart  2017 ). Accordingly, employees can arrange their time and energy with adequate job autonomy when they are WFH. They can deal with tasks under the best working status and promote work productivity and quality. On the other hand, WFH may lead to high job demand, which decreases job performance. Job demands are typically operationalized in terms of quantitative aspects, such as workload and time pressure (Hopkins and McKay  2019 ; Karasek and Theorell  1990 ). The boundary between working and leisure times becomes ambiguous when employees are WFH. Employees are usually pushed to work for longer hours and face high job demand, which is harmful to work productivity and quality. Therefore, assessing the influence of WFH on employees' feeling of their work completion is vaguer and more complicated compared with WFO, which leads us to propose our first hypothesis as a set of two alternatives:

Employees who are WFH are more satisfied with their job performance (i.e. job quality and job productivity).

Employees who are WFH are less satisfied with their job performance (i.e. job quality and job productivity).

Mediating role of job demand between WFH and job performance

On the basis of the JDCS model (Karasek and Theorell  1990 ), we tend to examine the differences of job fundamental characteristics and the moderating effect of social support on job performance between WFH and other working routines. WFH may increase job demand due to its possibility of pushing individuals to work for longer hours and increase the intensity of individuals. It will lead to a high investment of personal resources and bring adverse effects afterward.

First, WFH acquires more personal energy and time to invest in dealing with ‘communication via technology’, and employees may need to learn and equip with knowledge accordingly, including terms of using WFH tools and methods of collaboration (Yang et al. 2021 ). Moreover, employees may face the risks of technology fatigue or crash, which may result in negative psychological effects of misinformation and putting off work accomplishments (Khan  2021 ). Second, when employees need to continue to work beyond the regular working hours, they will inevitably face continuous additional work pressure, which makes them unable to relax and recover physically and mentally. Accordingly, more personal time and resources are demanded to invest in the job (Xie et al.  2018 ). Ayyagari, Grover and Purvis ( 2011 ) believed that WFH forms in such a convenient manner where employees may be required to stay on call for quarantine for a long time. WFH may influence employees' everyday life and lead to a perception of higher expectations for their working hours and intensity by their company and work loading. Ter Hoeven, van Zoonen and Fonner ( 2016 ) also verified this and reported that WFH may cost extra job demands from employees, including financial assets, energy, time and psychological capital. If those demands are too high, they may further make a series of workplace deviation behaviors, such as time‐encroached behaviors, to alleviate their loss of personal resources (Vayre  2021 ), consequently reducing their job performance.

The relationship between WFH and job performance is mediated by job demand.

Mediating role of job control between WFH and job performance

We further reason that the relationship between WFH and job performance is mediated by job control. The most prominent advantage of WFH is regarded as flexibly anytime and anywhere, which can significantly enhance employees' sense of job control and autonomy (Richardson and Thompson  2012 ). Mazmanian, Orlikowski and Yates ( 2013 ) found that employees who complete work tasks through WFH would have increased perceived work control and work flexibility. WFH can also enhance job autonomy in respect of task arrangement, work manner and task order (Mazmanian, Orlikowski and Yates  2013 ). Studies have also verified that WFH will promote employees' benefits in the field of the family via a more flexible and adaptable arrangement (Dockery and Bawa  2018 ). As a result, it can balance their work and family duties concerning the different daily situations and perform well (Tønnessena, Dhira and Flåten  2021 ).

The relationship between WFH and job performance is mediated by job control.

Moderating role of employers' anti‐epidemic policy

Social support is characterized by helpful relations with supervisors and coworkers (Mayo et al.  2012 ). Previous evidence has argued that a lack of support from employers when applying WFH may lead to a series of problems and thus reduce job performance (Palumbo  2020 ). According to the JDCS model, social support often buffers the effects of job demands and job control on the work‐related outcomes of employees (Johnson and Hall  1988 ). We investigate the moderation effect of social support on the relationship between job demand/control and job performance.

First, WFH may lead to isolation among employees if they have fewer interactions with their coworkers, supervisors and managers. Second, employees may not get recognition and support when needed, which may lead to employees' dissatisfaction, as their social needs cannot be fulfilled by WFH (Marshall, Michaels and Mulki  2007 ). Another negative consequence is receiving less recognition for achievements because exhibiting their work achievements is more difficult when all communication is conducted electronically (Zhang 2016). The limitation exists because when employees are WFH, they usually submit their work when it is ready. However, their manager may not see the process involved in producing a deliverable; some employees may work overtime, but their work is only judged by the result, not by the difficulties they overcome. Thus, policies or strategies should be implemented to enhance employers' feeling of embeddedness, not only for the sake of job performance but also for their well‐being and sustainability of human resourcing of organizations.

Particularly, considering the context of the epidemic, support actions from employers aiming to be anti‐epidemic and protect employees will be essential to improve the positive consequences of WFH. Thus, the present paper takes employers' anti‐epidemic policy as prominent social support worthy of examining. Indeed, some Chinese companies coined proactive guidance and support for employees (Reeves et al.  2020 ). The support reportedly helped employees feel less stressed, experience more positive feelings toward their leader and their team, and created an atmosphere of trust and understanding that motivated them to apply themselves more fully to work (Xu and Thomas 2011 ). In this case, we suggest that a moderating effect of the employers' anti‐epidemic policy is significantly observed on the influence of WFH on job performance. Figure  1 shows the conceptual framework.

Social support moderates the relationship between job demand and job performance, such that the relationship is weaker when social support is high rather than low.

Social support moderates the relationship between job control and job performance, such that the relationship is stronger when social support is high rather than low.

The conceptual framework

Our sample was collected from China. It is the first region where the government applied a lockdown policy, which encouraged employers to organize their employees to WFH to mitigate the massive health crisis. Nevertheless, in March 2020, due to the sound control of COVID‐19 spread, after only a few months' lockdowns, Chinese citizens were able to return gradually to their normal work–life routine. As a result, some employees were WFH, and some of them returned to their normal work routine. Different from the previous research conducted in a limited number of industries or focusing on a particular occupation group, such a situation provides us a unique opportunity to design comparison research to understand early, initial reactions of a wide range of occupational groups and industries toward WFH and its social effect in the epidemic context.

Data were collected via an online survey, provided by a Chinese survey company called Wenjuanxing ( www.wjx.cn ), a platform providing functions equivalent to Amazon Mechanical Turk. Research on WFH confronts a widely noted difficulty in managing data face‐to‐face, especially during this particular epidemic term. Thus, we chose to issue and collect the questionnaire online.

We initially did a pilot survey on 1 March 2020, with 100 observations. Later, after adjustments to the questionnaire, we issued the formal study of 5 March 2020, a month after the earliest date for work resumption according to the Chinese government. Thus, some employees were returning to workplace (RTW), and some continued WFH after Chinese New Year. As mentioned before, this particular time allows us to do a comparison study that covers various types of occupation and organization to seek the differences between WFH and RTW when society is confronted with a significant public health emergency. After collecting data for two weeks, we gathered 1342 observations.

Furthermore, to alleviate the self‐selective bias caused by participants passively excluded from WFH due to lacking necessary conditions, we took the inclusion criteria that required the participants to be equipped with requirements of WFH, such as essential online tools and Internet access. We identified the qualified group by asking, ‘Do you think you have the qualified conditions to be working from home (e.g., possesses Internet access, laptop, smart phone, software, and apps)?’ Then, we selected those who answered yes. After cleansing invalid data, the final sample consisted of 861 individuals, among which 442 claimed that they were WFH, and 419 were RTW.

Participants

Our sample comprised participants who were portrayed as young and received a high‐level of education, who were aged around 31–35 on average. The participants were 44% male. The majority of the participants were qualified with undergraduate degree. Particularly, 9.98% of the participants were married without children, 58.65% were married with children, 30.89% were single without children, and 0.4% were single with children. Around half of the participants (50.41%) worked for private enterprises, 16.7% worked for state‐owned enterprises, 15.21% worked for foreign companies, and others worked in government or public institutions. The participants at management positions accounted for 41%. Those who had marketing duties accounted for 31%. Others had positions in R&D. The participants worked for 9.36 days on average after the Chinese New Year (also the deadline of the epidemic blockade), and 71% of them had experience of training or education while WFH. The participants were from 16 places in China, the largest portions were from Guangdong Province (13.43%), Shanghai (7.66%), Shandong (6.15%), and Jiangsu (6.15%).

Dependent variable

Job performance was measured by two items adopted from a structured measurement coined by Viswesvaran, Ones and Schmidt’s ( 1996 ) measurement of job performance (overall job performance, productivity, and quality). We applied the two dimensions of job performance, namely, ‘productivity’ and ‘quality’, which were examined by self‐evaluation questions: 1) In terms of productivity, how do you evaluate the quantity or volume of work produced today (e.g. number of transactions completed)? 2) In terms of quality, how do you feel about how well the job was done today (You can consider several aspects of the quality of tasks completed, including lack of errors, accuracy to specifications, thoroughness, and amount of wastage)? The answers were measured using a Likert scale, from 1 (poor) to 5 (excellent). As a key self‐evaluation measurement of job performance, Viswesvaran, Ones and Schmidt’s ( 1996 ) instrument has been widely applied by following scholars in the fields of organizational behavior, psychology, and human resource management (Judge et al.  2001 ; Lee, Berry and Gonzalez‐Mulé  2019 ; Murphy  2020 ).

Independent variable

WFH was used here to identify the work status of respondents, with 1 representing WFH, and 0 representing WFO.

Job demand and job control were measured following Gonzalez‐Mulé and Cockburn ( 2017 ) work, which is a well‐documented instrument widely applied in research and referred to as the JDC model.

Job demand was measured by eight questions (e.g. ‘To what extent do you agree that your job requires working very hard?’ ‘To what extent do you agree that your job requires working very fast?’). The answer was measured using a Likert scale, from 1 (completely disagree) to 5 (completely agree; Cronbach's alpha = 0.83).

Job control was measured by seven questions (e.g. ‘To what extent do you agree that your job allows you to make a lot of decisions on your own?’ ‘To what extent do you agree that you have a lot to say about what happens on your job?’). The answer was measured using a Likert scale, from 1 (completely disagree) to 5 (completely agree; Cronbach's alpha = 0.75).

Social support was measured by employees' satisfaction on employers' anti‐epidemic policy. The survey question was, ‘Overall, are you satisfied with your employers’ anti‐epidemic support (e.g. financial support, emotional support from line managers, anti‐epidemic knowledge guides, and clear guidelines of WFH)?’ The answer was a dummy one, 1 representing yes, and 0 indicating no.

Control variables

First, we controlled for effective communication as a key factor that affects the quality of job performance, given that the majority of the literature has argued that ineffective communication is one of the greatest challenges of interpersonal collaborations mediated by ICTs in WHF (Wang et al.  2021 ). We controlled a set of communication factors in terms of ‘accurately delivered job content’ and ‘fully expressed the information’, among others. The answers were designed as a Likert scale, from 1 (completely disagree) to 5 (completely agree).

Furthermore, consistent with earlier studies, we controlled for difference of working hours, namely, the difference between daily working hours and today’s working hours, working experiences, normal daily working hours, daily number of colleagues they worked with, daily number of leaders they worked with, daily number of departments they worked with, daily commuting time, positions, age, gender, education, marital status, nature of employers, position levels, and days of starting work after the Chinese New Year. The definitions of variables are provided in Table  A1 .

Definition of variables

Analysis strategy

Our analysis consists of three steps. In Step 1, to test our hypothesis 1, we applied entropy balance and weighted mean difference Welch's t ‐test (mean after entropy balance matching) methods to compare the self‐evaluated job performance between WFH and WFO employees. Following the approach of recent papers on labor economics and health (Hetschko, Schöb and Wolf  2016 ; Kunze and Suppa  2017 ; Nikolova, 2019 ), our strategy includes 1) data preprocessing to form comparable groups of individuals as treatment and control group (treatment group: WFH employees; control group: RTW employees) by applying entropy balance, and 2) estimating the treatment effect after matching by Welch's t ‐test. We also reconfirmed the regression result (Hainmueller  2012 ).

In Step 2, we investigated the direct and mediating effects of job control and job demand on job performance (hypotheses 2 and 3). We applied the quasi‐Bayesian Monte Carlo method to test the mediating effect of job demand and job control, which is a technique to increase the robustness of the mediating test by employing a strategy of numerous repeated re‐sampling to build an empirical approximation of the sampling distribution and examine the indirect effects by constructing the confidence intervals (CIs; Imai, Keele and Tingley  2010 ). We used the package ‘Mediation’ for causal mediation analysis. In addition, to confirm the validity and reliability of mediating hypotheses results, we used structural equation modeling (SEM) as robustness check, with package ‘lavaan’ to assess the mediating effect of job control and job demand on the relationship between WFH and job performance.

In Step 3, to test the moderating effect of social support, we applied hierarchical regressions at the final step by following the classical approaches to seek the significance of interactions in a set of model tests.

All the analysis is conducted with software R.

Before testing the hypotheses, a benchmark test of a binary correlation matrix is presented in Table  2 . The overall coefficient is not high, and a variance inflation factor was performed at below 10, demonstrating low multicollinearity.

Variables correlation matrix

Influence of WFH on self‐reported job performance (hypotheses1a and 1b tests)

Before proceeding to test hypothesis 1 in Step 1, we first applied the entropy balance and weighted mean difference (mean after entropy balance matching) methods. The quality of entropy balance matching combined with a data description is summarized in Table  4 . Before matching, WFH employees worked for <2.7 h daily on average compared with their pre‐daily working hours. Employees who had returned to work worked <0.53 h on average than their current daily work. After matching, this difference was reduced. WFH employees are used to having less colleagues to work with (mean: WFH = 2.91, RTW = 3.18), are less likely to work at back office (mean: WFH = 0.10, RTW = 0.18), are younger (mean: WFH = 2.62, RTW = 2.77), are less likely to be married and have a child or children (mean: WFH = 0.52, RTW = 0.56), and are more likely to be single and without a child or children (mean: WFH = 0.36, RTW = 0.26). In addition, WFH employees indicated that they started working after Chinese New Year a day later than WFO employees (mean: WFH = 9.36, RTW = 11.09). In particular, WFH employees experienced better interpersonal communication than RTW employees (mean: WFH = 2.74, RTW = 2.67). In entropy balance matching, we matched all conditioning variables, and the bias of each matched variables was reduced to nearly 0, supporting good quality of entropy balance matching. Moreover, differences in mean and variance between the treatment and control groups were largely reduced after weighting (see in Table  A2 ).

Causal mediation analysis of job control and job demand

^ p  < 0.1; * p  < 0.05; ** p  < 0.01; *** p  < 0.001.

Descriptive statistics before treatment, selected covariate variables, before and after matching

Then, we verified hypothesis 1 by measuring the ATT under the balanced matching conditions in Table  3 . After matching, the results for hypothesis 1 are presented in Tables  5 and ​ and6. 6 . The results show that WFH employees are more satisfied with quality (mean: WFH = 4.56, RTW = 4.11, p  < 0.01). In addition, WFH employees feel less satisfied with productivity (mean: WFH = 3.86, RTW = 4.05, p  < 0.01). Hypotheses 1a and 1b were supported.

Treatment effect of WFH before and after entropy balance matching

* p  < 0.05; ** p  < 0.01; *** p  < 0.001.

Regressions on satisfaction with job performance (quality)

^ p  < 0.1; * p  < 0.05; ** p  < 0.01; *** p  < 0.001; Standard errors in parentheses.

Regressions on satisfaction with job performance (productivity)

* p < 0.05; ** p < 0.01; *** p < 0.001; Standard errors in parentheses.

Mediating role of job demand and job control (hypotheses 2 and 3 tests)

Changes in job demand and job control can be observed from Step 1 in Tables  5 and ​ and6. 6 . Under balanced matching conditions, WFH employees experience a significantly higher level of job control (ATT: WFH = 3.67, RTW = 3.58, p  < 0.05). More specifically, such change is noteworthy in the job control of ‘talking right’ (con2; ATT: WFH = 3.24, RTW = 3.06, p  < 0.05) and job control of ‘working rate’ (con5; ATT: WFH = 3.69, RTW = 3.16, p  < 0.001). In terms of job demand, WFH employees experience a significantly lower level than RTW employees (mean: WFH = 3.37, RTW = 3.46, p  < 0.001). The difference is obviously observed in terms of ‘long periods of intense concentration’ (dem3; ATT: WFH = 3.57, RTW = 3.73, p  < 0.05) and ‘hecticness of the job’ (dem4; ATT: WFH = 3.25, RTW = 3.48, p  < 0.01). These results imply that WFH may lead to changes in job control and job demand, which may intermediately affect job performance.

Therefore, in the second step, we tested the mediating effect by applying the quasi‐Bayesian Monte Carlo method in Table  4 . The results show that in terms of quality, the mediating effect of job control and job demand is confirmed as statistically significant (job control = 0.14, p  < 0.001; job demand = −0.02, p  < 0.01). The proportion of mediating effect on total effect is around 23.72% and 4.33%. We also tested the mediating effect of the important items of job control and job demand. We find that the job control on ‘working rate’ (con5; 0.12, p  < 0.10, prop. mediated = 21.05%), job demand on ‘long periods of intense concentration’ (dem3; −0.03, p  < 0.01, prop. mediated = 5.3%), and ‘hecticness of the job’ (dem4; −0.03, p  < 0.05, prop. mediated = 6%) positively mediate the relationship between WFH and satisfaction with quality.

In terms of productivity performance, the mediating effect of job control and job demand is supported (job control = 0.03, p  < 0.05, prop. mediated = 16.4.5%; job demand = −0.03, p  < 0.01, prop. mediated = 21.25%). However, it is noticeable, unlike in the domain of quality, that the mediating effect of job control and job demand contributes to the direct impact of WFH. Such mediating effect trades off the direct influence of WFH on satisfaction with productivity. Items such as job control on ‘working rate’ (con5; 0.01, p  < 0.05, prop. mediated = 5.88%) and job demand on ‘long periods of intense concentration’ (dem3; −0.03, p  < 0.01, prop. mediated = 20.11%) mediate the relationship between WFH and satisfaction with productivity. In this case, hypotheses 3 and 4 are fully supported.

In addition, the robustness check results via SEM analysis (both classical and bootstrap approach is used) is consistent with the quasi‐Bayesian Monte Carlo analysis. Accordingly, hypotheses 3 and 4 are supported as well (see details in Tables  A3 and ​ andA4 A4 ).

Robustness check of mediation effect by structure equation modelling

Moderating role of social support (hypotheses 4a and 4b tests)

Tables  5 and ​ and6 6 present the results of the moderating analysis of social support by applying hierarchical regressions. The results from the first four regression models consider the direct impact of WFH, job control, job demand and social support on self‐reported job performance as benchmark (Models 1–4 in Tables  5 and ​ and6). 6 ). Models 5–7 test the moderating effect of employers' social support on the relationships between job control, job demand and social support with job performance. We initially find that the social support is significantly positively related to satisfaction with quality (0.16, p  < 0.001) and productivity (0.27, p  < 0.001). Toward the moderating effect of employers' anti‐epidemic policy, we find the interaction terms of job demand*social support to be only significant on the regressions on satisfaction of productivity (−0.07, p  < 0.05). That is, hypothesis  4a is supported.

Overall, the results of testing the hypotheses are shown in Table  7 and Figure  2 .

Results of hypotheses

* p < 0.05; ** p < 0.01; *** p < 0.001.

The hypotheses results presented in the conceptual framework

Discussion and conclusion

In responding to the inconsistent findings on the impact of WFH on job performance, the present paper found that WFH helps promote job performance in terms of quality but leads to poor job performance in terms of productivity, which indicates that WFH may not always play an ‘either‐or’ (positive or negative) role, as previous theories suggest. To explore the causal mechanism underpinning the findings, based on the JDCS model, we found that WFH affects job performance via job demand and control path, moderated by social support, which indicate that WFH leads to flexibility, and employees have more autonomy to work at any timepoint per day to finalize their job. They usually choose the timepoint to conduct work when they have a desirable working condition, consequently cultivate focus, concentration and creativity (Hunter  2019 ). Accordingly, job quality can be enhanced. Despite a good job quality, WFH employees devote higher job demand. Thus, it is not conducive to job productivity than WFO employees. In addition, we found the positive moderating role of social support from organizations to enhance job performance during epidemic crisis.

Theoretical implications

The present paper aims to contribute in several ways. Our study extends the JDCS model under the context of COVID‐19 by investigating whether WFH can render the change in job control and job demand and exert influence on employees' job performance with the moderating effect of employers' support. The JDCS model can also help explain why WFH plays a mixed role to affect job performance. Prior studies have mainly qualitatively discussed changes to the way that individuals work during the COVID‐19 pandemic (Wang et al.  2021 ), the advantages and disadvantages of enforced WFH (Hallman et al.  2021 ; Purwanto 2020 ), ICT functions that enable to offer affordance to satisfy WFH targets (Waizenegger et al.  2020 ), and the way to provide a resource for WFH (Hafermalz and Riemer 2021 ). Research that indicates why WFH can affect employees' work‐related outcomes, particularly with empirical evidence, is limited. By applying a sample collected in China, we investigated two paths (i.e. job demand and job control) and a boundary condition (support) of the relationship between WFH and job performance.

Our results show that job control and job demand positively mediate the relationship between WFH and job performance. The increased job control and decreased job demand by applying WFH can be considered one of the main reasons WFH helps enhance job quality. This finding is notable because this study tends to clarify the mixed mechanism that WFH affects work‐related outcomes from the perspective of job characteristics and provides a theoretical framework. In terms of job productivity, we find that the increased job control and decreased job demand trade off the negative effect of WFH on productivity. Therefore, when explaining why WFH compared with WFO varies in job performance, the verified mediating effect of job control and job demand underpinned by the JDCS model can only account for job quality enhancement, rather than sufficiently support why WFH lowers job productivity.

The present paper also articulates the specific job control (‘talking right’ and ‘work rate’) and job demand (‘a long time of intense concentration’ and ‘hecticness of the job’) items are vital factors in performance enhancements. On the basis of such findings, we can presume that the ‘talking right’ enhanced by WFH implies that the enforced ‘physical distance’ may shorten the ‘power distance’ inscribed in hierarchical structure, because ICT enables communication flattening information transmitting in traditional stratified management. Reciprocally, such physical distance reduces redundant commands from managers, and workplace distractions trigger WFH employees to have more autonomy on ‘working rate’. Thereafter, in the wake of alleviations on ‘a long time of intense concentration’ and ‘hecticness of the job’, performance is enhanced.

Furthermore, we applied entropy balance matching, a method that has been regarded with more advantages for controlling self‐selection bias in quasi‐experiment research. Future studies could also adopt entropy balance matching to control self‐selection from process control, especially in the crisis context.

Empirical and managerial implications

Empirically, post COVID‐19, WFH may become a vital HRM strategy. According to the Gartner CFO Survey (2020), 74% of companies plan to shift some of their employees to remote working temporarily. Our findings may imply several valuable tips for organizational employers and employees if one wants to accommodate employees to WFH for the long term. We suggest that sustained and pragmatic WFH policy in terms of ‘set working hours’ and ‘taking regular breaks’ should be designed to reduce job demands, such as ‘a long time of intense concentration’ and ‘hecticness of the job’. Furthermore, employers may leave employees more empowerment on scheduling, enhance the equality among different hierarchy people, and avoid lengthy and discursive commands while working to improve the ‘talking right’ and ‘work rate’ autonomy for employees. In addition, social support is found to be a critical boundary condition between WFH and job characteristics. Thus, it is vital that sound and feasible epidemic policies, such as providing personal protective equipment, a financial sponsored program, psychological counselling and support, are put in place and executed as crucial responsibilities (Shani and Pizam  2009 ). And finally, employers need to be aware that more resources should be available for increased virtual collaboration needs as WFH has now taken hold and will be around for a long time in the future.

Limitation and future research perspectives

First, even though in the present study we have controlled for a wide range of variables that may potentially relate to job performance, inevitably, it still misses some relevant variables. For example, even though we have involved communication factors under control, technology fatigue may still contribute significantly on change of job demands and subsequently affect job performance (Yang et al. 2021 ). Second, our dataset is a cross‐sectional one and we asked employees to rate job performance rather than multilevel respondents. The absence of lagged performance data restricts the possibility of examining the long‐term effect of WFH on job performance and relationships between the variables of interest. As already noted, the current sample was collected at the early period of ending epidemic lockdown. By applying the cross‐sectional model, identifying the potential time variance (e.g. honeymoon effect) from the targeted relationship is difficult. Thus, future studies should adopt panel data and compare the present study to test for robustness.

This work was supported by the National Natural Science Foundation of China (grant number 72102033); Shanghai 2020 Science and Technology Innovation Action Plan (grant number 21692102600); the Fundamental Research Funds for the Central Universities of China (grant number N2206012); the Humanities and Social Science Foundation of the Ministry of Education of China (grant number 21YJC630153); the Social Science Foundation of Liaoning in China (grant number L21CGL013).

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, ‘Working from home vs. working from office in terms of job performance during COVID‐19 pandemic crisis: evidence from China’.

Biographies

Jingjing Qu is an associate professor at Shanghai AI Lab, China. Her research interests include artificial intelligence governance, artificial intelligence technology innovation and well‐being of entrepreneurs.

Jiaqi Yan is a lecturer at School of Business and Administration of Northeastern University. He received his PhD degree from Tongji University and studied as a joint PhD student at the University of Sydney. His research interests include human resource management, hospitality management and entrepreneurship.

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The Aggregate Impact of Working from Home

Work from home (WFH) has surged in America, rising from 5% of workdays in 2019, peaking at about 60% in May 2020 during the lockdown, to stabilize at about 27% by May 2023. This five-fold increase in working from home, including both full time remote and part time telework, has been possibly the largest change to US labor markets since World War II. This WFH surge has generated major economic and policy questions over the impact of this on many areas of the US economy. This project will investigate the impact of this WFH surge on the aggregate US economy and labor market, arising from the impacts on productivity (which could be positive or negative), and on labor force participation. These questions are important academically, for monetary and fiscal policymakers, for businesses and managers, and for investors planning for the impact of WFH on goods and labor markets.

This project has three major strands to advance research on this topic. First is the Survey of Working Arrangements and Attitudes (SWAA) which will collect detailed WFH information for around 8,000 working Americans a month aged 20 to 64 on current practices, intentions and impacts on lifestyle, productivity and living arrangements. This provides detailed monthly data on exactly the working patterns across regions, industries and occupations across the US. Second, the project will also develop an employee-employer dataset from a leading US payroll processing firm to examine where people live and work pre and post pandemic. Payroll data usually has accurate home and work location data, and by examining a panel of employees and individuals it is possible to examine impacts of WFH on locational choice and infer WFH patterns. Third, the team will examine the impact of working from home on aggregate US productivity and worker welfare using a general equilibrium model. This aim will provide results on individual workers? relative productivity while working from home and then enable counterfactual exercises to see how economy-wide welfare and productivity would differ if, for example, we forced working from home back to the low levels from before the pandemic. This will be invaluable for considering some of the larger, long-run aggregate impacts of the roughly 5-fold increase in rates of working from home experienced post-pandemic.

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