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Open Access

Peer-reviewed

Research Article

Working from home and productivity under the COVID-19 pandemic: Using survey data of four manufacturing firms

Roles Conceptualization, Data curation, Formal analysis, Writing – original draft

Affiliation Graduate School of Economics, Waseda University, Tokyo, Japan

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Education and Integrated Arts and Sciences, Waseda University; The Research Institute of Economy, Trade and Industry, Tokyo, Japan

Roles Conceptualization, Data curation, Methodology, Writing – original draft

Affiliation Business School, Doshisha University, Kyoto, Japan

Affiliation Faculty of Political Science and Economics, Waseda University; The Research Institute of Economy, Trade and Industry, Tokyo, Japan

• Ritsu Kitagawa, 
• Sachiko Kuroda, 
• Hiroko Okudaira, 

• Published: December 23, 2021
• https://doi.org/10.1371/journal.pone.0261761
• Reader Comments

The coronavirus disease 2019 (COVID-19) pandemic has impacted the world economy in various ways. In particular, the drastic shift to telework has dramatically changed how people work. Whether the new style of working from home (WFH) will remain in our society highly depends on its effects on workers’ productivity. However, to the best of our knowledge, the effects of WFH on productivity are still unclear. By leveraging unique surveys conducted at four manufacturing firms in Japan, we assess within-company productivity differences between those who work from home and those who do not, along with identifying possible factors of productivity changes due to WFH. Our main findings are as follows. First, after ruling out the time-invariant component of individual productivity and separate trends specific to employee attributes, we find that workers who worked from home experienced productivity declines more than those who did not. Second, our analysis shows that poor WFH setups and communication difficulties are the major reasons for productivity losses. Third, we find that the mental health of workers who work from home is better than that of workers who are unable to work from home. Our result suggests that if appropriate investments in upgrading WFH setups and facilitating communication can be made, WFH may improve productivity by improving employees’ health and well-being.

Citation: Kitagawa R, Kuroda S, Okudaira H, Owan H (2021) Working from home and productivity under the COVID-19 pandemic: Using survey data of four manufacturing firms. PLoS ONE 16(12): e0261761. https://doi.org/10.1371/journal.pone.0261761

Editor: Sergio A. Useche, Universitat de Valencia, SPAIN

Received: March 31, 2021; Accepted: December 9, 2021; Published: December 23, 2021

Copyright: © 2021 Kitagawa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Our datasets are proprietary and obtained in a legally restricted manner under confidentiality agreements and therefore cannot be made publicly available. However, the Institute of Empirical Research in Organizational Economics, Waseda University can allow any interested researcher with good intent to access the datasets for validation, replication, and other analyses feasible under legal restrictions. Please send an email to [email protected] with the title of this paper, your affiliation, and the purpose of the data access.

Funding: This research was supported by Grant-in-Aid for Scientific Research (B) from No.19H01502 (Kuroda) and (A) No. 18H03632 (Owan) from the Ministry of Education, Culture, Sports, Science and Technology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The coronavirus disease 2019 (COVID-19) pandemic has impacted the world economy in various ways. As one of the major changes, teleworking or working from home (WFH) has become widespread across countries. For example, Brynjolfsson et al. [ 1 ] suggest that in May 2020, approximately half of the workforce in the U.S. was WFH. Eurofound [ 2 ] showed that in July 2020, nearly half of all employees in EU countries worked from home. For Japan, the Cabinet Office [ 3 ] reported that the WFH percentage was 34.5% at the end of May 2020 (see also Morikawa [ 4 ] and Okubo [ 5 ]). Regarding other countries, see also Felstead and Rueschke [ 6 ], Pouliakas [ 7 ] and Delaporte and Pena [ 8 ]. While the WFH percentages may have varied across countries, two common features are observed: (1) many people reported that during the crisis, it was their first time WFH (for example, see [ 2 , 5 , 6 ]), and (2) the majority of workers WFH wished to continue the new working style even if there were no COVID-19 restrictions ([ 2 , 3 , 6 ]). This new global experience suggests that WFH may increase the welfare of workers and that the experience of WFH during the crisis may lead to growth in teleworking even after the crisis abates ([ 2 ]). However, the current evidence seems still mixed since several studies have found worsened psychological well-being associated with WFH (see [ 6 ] and Xiao et al. [ 9 ]), while enhanced well-being is reported in particular for those who are able to pursue their job at home (see Kroll and Nuesch [ 10 ], Bellmann and Hubler [ 11 , 12 ]).

This pandemic-driven WFH has dramatically changed people’s way of work, and it is crucial to sustain production during this ongoing crisis. Whether the new style will remain in our society highly depends on its effects on workers’ productivity. However, the effects of WFH on productivity are still unclear (OECD [ 13 ]). For example, Bloom et al. [ 14 ] found that WFH had a positive effect on call center workers’ productivity and reduced turnover. While the paper ([ 14 ]) reported evidence based on data collected before the COVID-19 pandemic, Emanuel and Harrington [ 15 ] also found a positive effect on the productivity of call center workers during the COVID-19 crisis. On the other hand, Morikawa [ 4 ] showed that the mean WFH productivity relative to working at the usual workplace was approximately 60% to 70% in Japan, and 82% of workers reported a decline in productivity in a WFH environment during the COVID-19 crisis (Felstead and Rueschke [ 6 ] reported mixed results by analyzing broader occupations in the U.K.: see also [ 16 – 18 ]).

Several studies have also reported both positive and negative effects of WFH on productivity, depending on skills, education, tasks or industry. For example, Etheridge et al. [ 19 ] reported that in the U.K., women and those in low-paying jobs suffered the worst average declines in productivity (see also [ 20 – 22 ]). The paper also reported that declines in productivity are strongly associated with declines in mental well-being (see also Bartik et al. [ 23 ], Dutcher [ 24 ], De Sio et al. [ 25 ], Escudero-Castillo et al. [ 26 ] and Oakman et al. [ 27 ]). On the other hand, papers report positive characteristics of teleworking, such as ncreased efficiency and a lower risk of burnout (see, for example, Baert et al. [ 28 ]).

Physical health is also the mediating factor for the productivity effect of WFH, and musculoskeletal symptoms are often discussed as a problem with WFH. While Moretti et al. [ 29 ] and Yoshimoto et al. [ 30 ] document that workers suffer from musculoskeletal issues due to WFH during the pandemic (see also [ 31 ]), Aegerter et al. [ 32 ] find the effect of WFH on neck pain and disability to be limited, and Seva, Tejero, and Fadrilan-Camacho [ 33 ] show that musculoskeletal symptoms had no significant effect on the productivity of telecommuters. In summary, although there has been a rapid accumulation of studies on WFH and productivity, the reported evidence is mixed, and we believe that additional evidence on when WFH is productivity-enhancing is needed.

In this study, we try to contribute additional evidence on the effects of WFH by using data from our original employee-level survey conducted in cooperation with four large listed manufacturing companies in Japan from April to June 2020. Specifically, we assess within-company productivity differences between those who work from home and those who do not, along with identifying possible factors of productivity changes due to WFH.

On April 7, 2020, the Japanese government declared a countrywide state of emergency. Although the state of emergency ended on May 25, the request for self-restraint on movement between prefectures was extended until June 19. In the meantime, the government asked firms to let workers work from home as much as possible. According to the panel survey conducted by the Japan Institute for Labour Policy and Training (JILPT) (2020), the number of WFH workers rapidly increased from early April and peaked in the second week of May 2020. It then started to decline after the state of emergency was lifted at the end of May and dropped significantly by the end of July. Notably, although the government declared a state of emergency, it was only on a request basis and was not mandatory; therefore, the final decision on whether to introduce WFH was made completely at the discretion of employers. Moreover, many Japanese firms allowed each workplace to individually decide whether to use WFH. Therefore, even in the same firm, while workers in some units worked entirely from home, workers in other units had to commute to the office even though both groups of workers performed similar tasks. The variations in WFH within the same company enable us to investigate whether there are productivity losses or gains due to WFH. However, because companies and middle managers had the discretion to comply with or to defy the official request, the decision to opt for WFH may be endogenous if workers with specific unobserved traits or roles in the workplace tended to be chosen for WFH. We mitigate this concern over endogeneity in two ways, which we explain as part of the empirical strategy in Section 3.

The survey we use includes questions on subjective productivity and the perceived factors of productivity losses, allowing us to investigate the possible determinants of deteriorations in productivity. It also contains questions on mental health and the perceived advantages and disadvantages of WFH, making it possible to examine the relationship between WFH and workers’ mental health.

Our major contributions are threefold. First, using employee survey data with relatively high response rates, we exploit the heterogeneities among workers within the same companies. Specifically, we identify the effects of WFH on productivity within the same company and within the same occupation, which vary depending on the number of days spent WFH. Focusing on specific companies also allows us to exclude the effects of differences in productivity, labor-management relationships, and organizational support for WFH among firms. Based on our analysis, workers who worked from home experienced a productivity decline compared with those who did not. Second, owing to the rich information available in our original surveys, we could identify the potential factors that determine deteriorations in productivity due to WFH. We find that poor WFH setups and communication difficulties are the major reasons for productivity losses. In addition, although the reasons above are common features of all occupations, we find that the major reasons that reduce productivity the most differ by occupation. Third, we complement our findings by analyzing the impact of WFH on mental health. We find that the mental health of WFH workers is significantly better than that of workers who are unable to work from home. Our results suggest that if appropriate investments in upgrading WFH setups and facilitating communication can be made, WFH may improve productivity by improving employees’ health and well-being.

One caveat is that our sample is not representative of Japanese workers, and this should be noted as a limitation, especially when discussing policy implications. Our sample is limited to those in the manufacturing sector. The average worker in our sample is more educated, working in larger and more male-dominated organizations, and more likely to be in technical and professional jobs than the general population. However, the sample includes many occupations from those with the highest to the lowest likelihood of working from home, which makes it suitable for examining the heterogeneity of the effect of working from home in the same management and business environment.

The remainder of this paper is organized as follows. Section 2 describes our data, and Section 3 presents our quantitative methods. Section 4 explains the results, and Section 5 concludes.

We use data retrieved from our original survey on WFH productivity during the COVID-19 pandemic, which was conducted in cooperation with four listed manufacturing companies in Japan (Companies A, B, C, and D) from April to June 2020. Companies A, B, and D are chemical manufacturing companies, while Company C is an automobile manufacturing company. Companies A, B, and D have approximately 8,000, 7,000, and 27,000 employees, respectively, while Company C has more than 30,000 employees on a consolidated basis. The survey was conducted after each company responded to the authors’ proposal to examine the effect of working from home using a common questionnaire. Therefore, each company’s survey period, target, and questionnaire are somewhat different from the others because they were initiated by each company’s management and tailored to its needs. Some questions were modified to be consistent with similar questions included in its regular employee survey (see Table 1 for major differences across companies). The employees were told that the responses collected would be analyzed anonymously by the department in charge and that only aggregated figures for each organization would be shared with their superiors following each company’s data protection and privacy policy. The authors were given access to the anonymized dataset after the companies’ internal use. This research has been judged as not requiring review by the institutional review board of Waseda University, where the corresponding author works.

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https://doi.org/10.1371/journal.pone.0261761.t001

The survey was administered to both white- and blue-collar employees (Companies A, B, and D) or white-collar employees (Company C). Hence, the Company C sample does not include blue-collar workers, who regularly worked at the factory during the survey period, resulting in the smaller proportion of “no WFH” responses compared to the other companies. The employees of Companies B and D also included those of subsidiary companies. All employees of the four companies were asked to complete the survey. The survey included questions on topics such as the number of days spent WFH per week, productivity (presenteeism; details will be explained in Section 2.1.1.) before and after the state of emergency, the perceived causes of productivity losses, the respondents’ mental health status (details will be explained in Section 2.1.2.), the perceived advantages and disadvantages of WFH, and the respondents’ occupation, job grade, division, and basic individual characteristics. The response rates vary across the companies, ranging from 43% to 91%. The total sample size was 24,175, which fell to 22,815 after excluding invalid responses. Because the survey asked about the respondents’ productivity level both before and after the state of emergency, our analyses could rule out the time-invariant component of individual productivity.

The survey included a question on the number of days spent WFH per week during the reference period. We consolidated the answers into four categories based on the number of days spent WFH: none, once or twice, three or four times, and five times a week (i.e., exclusively WFH). Table 1 shows the percentage of employees who worked from home by the number of days worked from home per week on a company-by-company basis. It shows that among employees within the same company, there is variation in the number of days spent WFH.

Moreover, the percentage of workers who completely worked from home, i.e., those who worked from home five days a week, ranged from approximately 8% to 22% across the four companies. On the other hand, the figures show that approximately 40% to 50% of employees of Companies A, B, and D and 10% of employees of Company C worked entirely at the office. Note that this share of employees, not WFH, is low for Company C because it asked only white-collar employees to complete the survey.

2.1 Outcome variables

2.1.1. productivity..

In our survey, productivity was measured based on answers to the modified version of the Health and Work Performance Questionnaire (HPQ), which was developed by the World Health Organization (WHO) and used to measure subjective productivity (presenteeism). Our productivity measurement was conducted based on two-stage questions following the WHO-HPQ. The first item asked respondents the following retrospective question: “( o ) n a scale from 0 to 10 where 0 is the worst job performance anyone could have at your job , 5 is the performance of average workers , and 10 is the performance of a top worker , how would you rate your usual job performance (in the one-year period) before the declaration of the state of emergency ?” This item aimed to determine the average level of productivity of individual employees in the pre-COVID-19 period. In the questionnaire used for Company A, however, the phrase “in the one-year period” in the parentheses was not included. This means that pre-COVID presenteeism may be underestimated for Company A if a much shorter preperiod is considered by its employees.

The second question asked respondents to also apply a “0 to 10” scale to grade their overall job performance for a specific period during the pandemic (the actual period varies from company to company between April 2020 and June 2020). Taking the difference between the answers to these two questions, we calculated the changes in productivity before and after the state of emergency, which allows us to account for unobserved heterogeneity among workers. Regarding Company D, the simplified University of Tokyo version of the one-item presenteeism scale (Presenteeism-UT), which aimed to reduce the number of questions based on the HPQ, was used. For Company D, the employee survey was conducted twice, first in early March 2020 before the state of emergency was declared and again in April 2020. Therefore, unlike the other companies for which presenteeism before the state of emergency was evaluated in a retrospective manner, for Company D, presenteeism was measured at two time points—before and after the state of emergency. Specifically, the Presenteeism-UT asked employees to “Suppose that 100% is your work performance when you are neither sick nor injured. Please evaluate your current work performance.” For the April survey, the question was changed to “Suppose that 100% is your work performance when you are neither sick nor injured before the state of emergency. Please evaluate your current work performance after April 8.” We standardized the responses to a 0–10 scale by dividing by 10.

We understand that the retrospective method used at Companies A-C might be problematic when the measurement error for the preoutcome correlates with the independent variable of interest, the number of days spent WFH per week (hereafter WFH in short), thus biasing the estimates for WFH coefficients. WFH may correlate positively or negatively with productivity changes. For example, those who worked from home may tend to understate their past productivity level, thereby overstating the productivity growth after the pandemic to pretend that they worked hard even in the pandemic. In this case, the coefficient estimate for WFH is overestimated in the presence of bias in the dependent variable. Alternatively, workers may overstate their past productivity level and understate productivity growth after the pandemic so that they can accuse their productivity decline to the pandemic. If this situation is more serious for those who worked from home, the coefficient estimate for WFH would be downward biased. Although we do not know which type of retrospective error is potentially more likely to arise, we believe that the possibility of such biases is relatively limited since in all four firms, the employees were explained that the individual responses were anonymized and would not be disclosed to the superiors. Therefore, workers should have had very limited incentive to manipulate their productivity level, if any.

We use this presenteeism measure as one of our main outcome variables. Higher values indicate less presenteeism (i.e., higher productivity).

2.1.2. Mental health index.

Another main outcome variable of this paper is employees’ mental health. In the survey, we asked respondents to “(p)lease answer the following questions concerning your health since [the start date of the reference period]” along with the following three questions about workers’ mental health: “I have been depressed,” “I have felt weary or listless,” and “I have felt worried or insecure.” The respondents were asked to choose from four options: “almost always,” “often,” “sometimes,” and “almost never.”

In 2015, the Japanese Industrial Safety and Health Law was amended to mandate firms with 50 or more employees in the workplace to conduct a Stress Check Program once a year to screen high psychosocial stress. The Brief Job Stress Questionnaire (hereafter, the BJSQ) is highly recommended to firms by the Ministry of Health, Labour and Welfare for screening. Using data of 7356 male and 7362 female employees in a financial service company who completed the BJSQ, Tsutsumi et al. [ 34 ] report predictive validity of the BJSQ by finding that employees identified as high stress using the BJSQ had significantly elevated risks for long-term sickness absence by the one-year follow-up. Note that the three questions used in this paper to measure employees’ mental health are the same as those included in the BJSQ. We coded these responses on a 1 to 4 scale and reduced the total scores from the three questions into one dimension by using correspondence analysis with the dimension with the highest eigenvalue being the mental health index. Correspondence analysis reduces the dimension of scales among a set of qualitatively similar categorical variables (see, for instance, [ 35 ]). Higher values indicate better mental health. This index is highly correlated with the simple sum of the total Likert-based scales (the correlation coefficient is approximately 0.95 across firms). Note that this variable is not available for Company A.

2.2. Covariates of interest

2.2.1. perceived factors affecting productivity and mental health..

The survey also asked respondents who worked from home during the reference period to choose potential factors that caused declines in their productivity. Specifically, the respondents were asked the following multiple-choice question: “what factors, if any, do you think lower productivity when working from home?” The choices were “ the inability to retrieve data from outside of the office because of security ,” “the inability to use exclusive equipment that is available only at the office , ” “poor WFH setups (e . g ., do not have own office space) , ” “lack of articulate orders and/or poor support from superiors , ” “poor workplace communication , ” “poor communication with clients , ” “fatigue from an excessive workload , ” “not feeling well physically (stiff shoulders , back pain , etc . ) , ” “feeling mentally under the weather , ” and “having distractions or responsibilities to deal with (such as kids who want attention , nursing care for parents , and other family responsibilities) . ” Note that some of the choices were missing in the questionnaire for Company D.

In the survey, we also asked WFH employees additional multiple-choice questions about workers’ perceived advantages and disadvantages of WFH on mental health. Specifically, we asked, “ While working from home , did you find any advantages (disadvantages) of WFH that may have improve (worsen) your stress , if any ?” The choices of advantages were “ no distractions and a quiet environment that facilitates a greater focus on work ,” “ can avoid frequent and/or unnecessary conversations with coworkers ,” “free from stress caused by annoying relationships with coworkers and bosses , ” “ improvement in IT skills ,” “ zero commuting and saving time on getting ready for work ,” “ being able to wear casual clothes ,” “ less fatigue and having a healthier condition ,” “ eating healthier meals ,” “ spending more time exercising ,” “ reducing alcohol consumption ,” “ having extra time for sleep and rest ,” “ less smoking ,” “ having extra time with family and friends ,” “ the ability to fit in household chores , parental care , and extra time with kids ,” “ better family relationships ,” and “ finding new hobbies due to the constraints on going out .”

The choices of disadvantages were “ project delay ,”, “ lack of coordination/communication in workplace ”, “ poor IT environment ,” “ musculoskeletal pain ,” “ eye strain ,” “ migraine ,” “ having to prepare meals ,” “ eating unhealthier meals ,” “ spending less time exercise ,” “ weight gain ,” “ increasing alcohol consumption ,” “ snacking ,” “ more smoking ,” “ disturbed sleep ,” “ decreased conversation and feeling alone ,” “ childcare due to school closure ,” “ nursing care for parents ,” “ worse family relationships ,” “ constraints on going out .” Some items in the advantage and disadvantage are conceptually paired in a sense that they represent the opposite of each other. In such a case, we take the difference between the advantage and disadvantage items and use it as an advantage variable. The variables created in this manner are “ can avoid unnecessary communication ,” “ free from annoying relationships with coworkers and bosses ,” “ exercising more ,” “ drinking less alcohol ,” “ smoking less ,” “ better sleep ,” “ better diet ,” “ better family relationship ,” and “ enjoying staying home .”

2.2.2. Functional roles.

Using the occupational classification of each employee, we categorized the employees into four functional roles: corporate , sales , R&D , and production . Production included not only blue-collar employees who engage in the production process but also white-collar employees who manage production and quality control. In the following, we divide our observations into subsamples by these four categories to investigate whether the possible causes that reduce WFH productivity may differ across functional roles.

S1 Table presents the descriptive statistics of each company.

3. Empirical strategy

3.1. main model.

This study used different identification strategies for the presenteeism and mental health variables. For presenteeism, our survey asked for a subjective assessment of productivity in March (i.e., prior to the declaration of the state of emergency) and in April or May (i.e., after the declaration), and we had one observation point for mental health. We first explain our approach to the former in this section and to the latter in the next section.

We can identify β using ordinary least squares (OLS) if the WFH term is orthogonal to the error term, conditional on individual characteristics. This assumption is likely to be violated if workers with specific unobserved traits or roles in the workplace tend to be chosen for WFH. If companies are more likely to allow more productive workers to work from home, the estimated β will be overestimated. Likewise, if less productive workers volunteer to work from home disproportionately more often than more productive workers, then the estimate for β will be underestimated.

In our case, the shock to WFH adoption was mostly exogenous. Similar to the context of previous studies on the impact of WFH after the pandemic, the declaration of a state of emergency in Japan had a large and less expected impact on WFH adoption. According to Table 1 , quite a large number of workers worked from home owing to the government’s request in April. More than half of the employees in our sample worked from home at least once a week. Importantly, however, the government’s WFH request was not mandatory. Because companies and middle managers had the discretion to comply with or to defy the official request, the decision to opt for WFH may still be endogenous.

As a result, our main sample is reduced to a cross-section of the first-differenced outcome variable. Δ z ijkt is the difference in the number of days spent WFH during the period between the two surveys, which is denoted by wfh_dif . For Company A, information on the number of days spent WFH before April is lacking. We replace Δ z ijkt with z ijkt under the assumption that a very small number of employees worked from home for a limited number of days before April.

In particular, we include the following terms as X ijkt : a female dummy, age category dummies, and dummies for job grades and divisions. Including dummies for job grades and divisions in Eq ( 2 ) essentially allows us to control for separate trends across different job levels and divisions. Controlling for such trends is important in the analysis of WFH after the pandemic because a worker’s occupation and functional and technical roles within the organization could correlate with her superior’s WFH choice for her. In other words, by including dummies for job grades and divisions, the coefficient β is identified mainly based on the variation within the division and job level where the variation in WFH is primarily caused by the preference and management style of the worker’s supervisor, which is less likely to be correlated with the worker’s productivity.

To the extent that our estimation model controls for the selection bias arising from such endogenous adoption of WFH, the estimate of β represents the causal impact of WFH adoption. One cause for concern is that some employees were transferred across divisions during the reference period. However, their functional roles rarely changed after the transfer, and the effect of the division within the same functional role was not expected to differ substantially.

The equation will be estimated company by company (companies A, B, C, and D) so that betas will be company-specific estimates of the productivity change associated with the WFH change.

3.2. Model for mental health

As discussed above, for our mental health variable, we have one observation point. Thus, taking the first difference is not feasible. There are two major confounding factors for the relationship between mental health and WFH. First, a worker’s low ability or productivity might make it difficult for his superior to allow him to work from home, and at the same time, his low evaluation could harm his mental health. Second, jobs requiring many face-to-face interactions or heavy responsibilities might not only prohibit WFH but also place a greater mental burden on workers.

To address such possible confounding factors, we include both pre-COVID productivity and division/job level dummies as controls. Controlling for differences in workers’ workplace and job level also allows us to account for technical or operational reasons underlying the WFH choice.

Furthermore, we argue that for mental health, endogeneity of WFH is less of a concern than for productivity. Namely, it is unlikely that workers with a specific mental health condition tend to be chosen for WFH because a person’s mental health condition is not known to her supervisor until it has deteriorated so much that her productivity has started being seriously affected or her doctor’s recommendation of sick leave or a job transfer is submitted. Even if the supervisor knows her subordinate’s mental health condition before it becomes this bad, it is not a priori obvious whether choosing WFH will be good or bad for her health.

With all efforts to reduce potential confounding factors, we estimate Eq ( 1 ) using OLS to make causal interpretations. However, we still cannot rule out the possibility of some bias due to selection (see, for example, Bubonya et al.[ 36 ]). It might be the case that employees whose mental status was most seriously damaged by WFH were less likely to respond to the survey request. In this regard, although we think the magnitude of potential selection bias should be smaller than other survey approaches taken in the literature, such as web-based online surveys, the results below shall be observed with reservations.

3.3. Analysis using the WFH sample

Given our previous discussion, we predict that the OLS estimates of the first-difference equation for presenteeism might be biased due to the endogeneity of WFH if unobservable factors that separate trends of presenteeism are correlated with the decision to work from home. To investigate our predictions, we have estimated both OLS and type II Tobit models (models with sample selection biases).

Note that we cannot take the same approach to sample selection due to worker decisions not to respond to survey requests because we do not have access to worker characteristics information for those who did not answer the questionnaire.

4.1. Frequency of WFH and productivity

First, we estimate Eq ( 2 ) without control variables to observe how the frequency of WFH affects productivity. The results are shown in Table 2 . Note that the variable wfh_dif is missing for Company A because information on the number of days spent WFH before April is lacking. The coefficient estimates of the difference in the number of days spent WFH for Companies B-D and the WFH dummies for Company A are all significantly negative.

https://doi.org/10.1371/journal.pone.0261761.t002

In summary, the results indicate that workers who worked from home experienced declines in productivity compared with those who did not. This adverse effect was considerably large for Company D, which may have resulted from the fact that the survey was conducted in late April, two weeks after the declaration of the state of emergency. At that time, many employees were forced to work from home without full preparation, which may have temporarily resulted in a large decline in productivity.

Table 3 shows the full model including other explanatory variables (i.e., Eq ( 4 )). For Company B, the first difference of the WFH days becomes statistically insignificant. On the other hand, although the magnitude of the estimates decreases, the frequency of WFH still negatively affects productivity for Company D even after controlling for various individual and job characteristics. Note that the level of WFH dummies is negative for both Companies B and D. For Company C, the magnitude of the first difference becomes even larger. However, the WFH dummy of 5 days is positive and statistically significant. We will reconsider this in the subsample analysis below.

https://doi.org/10.1371/journal.pone.0261761.t003

The estimated negative effects of WFH should be interpreted carefully because our dependent variable is measured subjectively and subject to measurement error due to retrospective bias. This is particularly worrisome if workers tend to overstate their past productivity level and thus understate productivity growth after the pandemic. However, we believe that the workers’ inventive to manipulate the report is limited due to anonymous treatment of the responses, as we discussed earlier. Furthermore, the consistent results across all four companies suggest that the specificity of workers’ incentives in given situations is unlikely to have affected our results.

The full model offers another causal parameter worth mentioning. The productivity losses are greater for employees in their 30s, 40s, and 50s in Companies A, C, and D. Young workers are not significantly affected by the shift to WFH presumably because (1) they are more familiar with online communication and recent information technology than their older counterparts and (2) they are assigned more specialized or solo tasks requiring less coordination; thus, their productivity is less constrained by WFH. These results may provide evidence that, on average, employees experienced declines in productivity from WFH. Below, we investigate what factors caused such declines in productivity.

4.2. Causes of productivity losses

To identify the causes underlying the productivity losses, we add as explanatory variables the responses to the question of what factors the respondents perceived as causing their productivity to decline. For Company D, slightly different wording was used for some questions, but what was being asked was essentially the same. However, a few questions were not available. Accordingly, “the inability to retrieve data” and “having responsibilities (childcare and/or nursing care)” are missing for Company D. Here, the sample is restricted to those who worked from home at least one day per week after the state of emergency. Any factors that are strongly correlated with productivity losses should be the main mechanism underlying the drop in productivity.

Table 4 reveals two important common channels. First, “ poor WFH setups ” have significantly negative coefficients for all companies, and “ the inability to retrieve data from outside the office ” is also negatively correlated with changes in productivity for Companies A and B. These results indicate that the lack of sufficient infrastructure for WFH hinders employee performance. Second, “poor workplace communication” and “poor communication with clients” are significantly negative for almost all companies. This result implies that new communication applications such as social networking services (SNSs), chat apps and conference calls cannot easily replace traditional communication methods such as face-to-face communication or phones and their role in meeting spontaneous, simultaneous or urgent needs for communication.

https://doi.org/10.1371/journal.pone.0261761.t004

The significance of the coefficients of the other variables varies across companies. We shall also note that “having responsibilities (childcare and/or nursing care)” is also negative and statistically significant for Companies A and C. During the state of emergency in April to May, a number of children did not attend school because of closures. Additionally, many daycare centers for elderly individuals have closed to avoid cluster infections of COVID-19. These closures have caused temporary loss of productivity for workers who needed to take care of their family members while working from home.

These results imply that the loss of productivity when working from home can be ameliorated by addressing those undesirable factors. In particular, the infrastructure for WFH can be relatively easily improved by appropriate IT investment or by financial support provided by companies to their employees to establish a better work environment at home. In the long run, further technological development of IT security and communication devices and learning by doing among workers will help find efficient ways to communicate within and across companies.

To address sample selection bias, we also estimated type II Tobit models (the maximum likelihood estimator and Heckman’s two-step estimator) to address potential selection into WFH as a robustness check. The estimation results did not provide evidence of selection bias and were qualitatively the same as the OLS estimation results.

4.3. Subsample analysis of causes

We now take a closer look at the causes of productivity losses by conducting subsample analysis. We divide the sample into four based on functional roles, i.e., corporate, sales, R&D, and production, and we estimate the model presented in Section 4.2.

Tables 5 – 8 present the main results. Once again, the factor that is fairly common to all four functional roles is “ poor WFH setups ,” and the coefficient estimates are significantly negative for most cases. Apparently, it may be more important for corporate and R&D jobs since the estimates are all significant, except in the case of Company A, where the estimates are significant only at the 10% level.

https://doi.org/10.1371/journal.pone.0261761.t005

https://doi.org/10.1371/journal.pone.0261761.t006

https://doi.org/10.1371/journal.pone.0261761.t007

https://doi.org/10.1371/journal.pone.0261761.t008

Now, we turn to the specificity of each functional role. For corporate jobs and sales jobs, “ poor workplace communication ” and “ poor communication with clients ” have significantly negative effects on productivity across companies, which is consistent with the intuition that corporate jobs and sales jobs intensively involve engagement in coordination and organization both within and outside the company.

This result is reasonable considering the nature of the tasks undertaken by employees who hold these roles. For sales jobs and R&D jobs, the coefficient estimate for “ the inability to retrieve data ” is significantly negative for Companies A and B, and the coefficient estimate for “ the inability to use exclusive equipment ” is significantly negative for Companies A and C. Once again, these results are reasonable since workers engaged in R&D tend to engage with confidential information such as patents. For production jobs, the estimate for “ poor workplace communication ” is significantly negative, except in the case of Company B, and this result is also fairly consistent with the duties and tasks of workers holding such jobs.

Across functional roles, there is a common factor of productivity losses, i.e., “ poor WFH setups ,” which calls for comprehensive support for all occupations to improve the WFH conditions that employees face. In addition, our results indicate that the most important factor in improving WFH productivity differs by occupation, suggesting that employers should recognize that the optimal investment priorities may differ across occupations.

4.4. Frequency of WFH and mental health

We next study the relationship between mental health and WFH by estimating Eq ( 1 ). Table 9 shows the results obtained from the regression of mental _ health i on wfh 2 d i , wfh 4 d i , and wfh 5 d i , controlling for pre-COVID WFH experience and productivity as well as basic individual and job characteristics. The variable wfh _ bf i is a dummy indicating an individual working from home at least one day in March before the state of emergency. Prepandemic productivity, prsnt _ bf i , is included to address the potential confounding factors between WFH decisions and workers’ mental health through productivity. With additional controls of divisions and job levels, we made every effort to account for potential confounding factors because the outcome variable is not the first difference, unlike the estimates for presenteeism; thus, unobservable worker characteristics could still bias the results.

https://doi.org/10.1371/journal.pone.0261761.t009

Overall, employees’ mental health seems to have a positive association with the frequency of WFH, implying that WFH could mitigate the mental deterioration of workers. The relationship is particularly significant at Company D, where the sample size is largest. However, the overall pattern is similar among the three firms.

The greatest concern for the result is sample selection, particularly in Companies B and D, where the response rate is lowest. It may be the case that employees whose mental health is damaged by WFH were less likely to respond to the survey request than those who benefit from WFH or those whose productivity is damaged by not WFH. Such selection could impose upward bias on the coefficient of WFH variables. Although we do not rule out this possibility, we observe a surprisingly similar pattern among the three firms whose labor-management relationships should vary. This implies that selection bias might be relatively small.

4.5. Costs and Benefits of WFH

To identify what factors contribute to improvements and deteriorations in mental health, we estimate Eq ( 1 ), adding as explanatory variables the responses to the question of what factors the respondents perceived as advantages and disadvantages of WFH and restricting the sample to those who worked from home after the state of emergency. We shall note that we estimated a sample selection model for mental health and WHF, but the evidence of selection bias was weak, and the estimates remained substantially identical.

The factors that have a strong association with better mental health, conditional on individual and job characteristics, should be the main benefits of WFH. Two potential benefits emerge from the results shown in Table 10 . First, the coefficients of “ better sleep ” and “ drinking less alcohol ” are significantly positive across companies. Second, “ can avoid unnecessary communication ”, “ facilitates a greater focus on work ”, and “ enjoying stay　home ” are significantly associated with better mental health for Companies C and D, although a similar pattern cannot be observed for Company B. Notably, “ zero commuting and saving time ” is significantly positive for Company D.

https://doi.org/10.1371/journal.pone.0261761.t010

The result suggests that WFH improves the quality of sleep presumably by reducing time to prepare for work and to commute. Interestingly, it also reveals that less drinking is positively associated with mental health. This may reflect the fact that workers find WFH benefits since they do not have to go for a drink with colleagues after work or because improved mental health due to WFH induces the workers to consume less alcohol. Additionally, due to fewer interruptions that would normally occur at the workplace, WFH allows for a quieter environment that can facilitate a greater focus on work. Although undesirable aspects of WFH are oftentimes emphasized by business practitioners, WFH may improve productivity by improving employees’ health and well-being.

This benefit due to the longer rest period enabled by WFH should have the same impact as shorter working hours. In fact, in the literature, there is some evidence of the benefits of shorter working hours. Using data on women working in manufacturing plants to produce artillery shells for the British military during the First World War, Pencavel [ 37 ] found that the hours-productivity profile exhibits a concave, nonmonotonic shape, implying that having a longer rest period could improve productivity when workers work excessive hours. Similarly, using single-company data on Japanese construction design projects, Shangguan et al. [ 38 ] showed that team productivity and the quality of work improved when working hours were reduced during the great recession.

In contrast, there seem to be negative factors for employees working from home that worsen their mental health. The coefficients for “ project delay ” and “ poor IT environment ” are significantly negative across companies. Remarkably, physical disorders such as “ musculoskeletal pain ”, “ eye strain ”, and “ migraine ” are significantly associated with deteriorated mental health. These findings are consistent with Moretti et al. [ 29 ] who claim that WFH causes musculoskeletal pain that reduces productivity (see also [ 30 , 31 ]).

These estimates suggest that employees are frustrated with delays in ongoing projects, possibly due to poor IT setups. As a result, frustration and poor IT setups might stress employees and damage both their physical health and mental health. This result presents the need to support the establishment of physically friendly WFH facilities and IT infrastructures.

4.6. Gender differences

There are some studies reporting a worse labor market outcome (Adams-Prassl et al. [ 20 ]) and larger declines in productivity among women than men (Etheridge et al. [ 19 ]) or with worse mental health (Felstead and Reuschke [ 6 ], Etheridge and Spantig [ 39 ]) for female workers during the pandemic. Most attribute the difference to women’s increased role in family and caring responsibilities (see also [ 40 ]). To this end, we paid special attention to female dummies in all of our analyses and conducted subsample analyses to examine whether women are affected by WFH during the pandemic in any different way than men. Surprisingly, we did not find any consistent gender differences among the four firms. For example, while the productivity of female employees was significantly higher than that of their male counterparts ( Table 4 ), their mental health was significantly worse in Company D ( Table 9 ). In contrast, the mental health of female employees was significantly better than that of their male counterparts in Company C, and we did not find any statistically significant gender differences in Companies A and B (Tables 4 and 9 ).

Expecting that there might be heterogeneity among female employees depending on the family structure (e.g., the number and ages of children), we conducted subsample analysis of Tables 4 and 10 with or without additional controls of the number and ages of children to see the effects of WFH by gender. However, we did not obtain any consistent differences between men and women among the four firms regarding the number and ages of children, or the costs and benefits of WFH on productivity and mental health, although there were some idiosyncratic gender differences that were specific to one firm.

One obvious reason for the difference reported in other studies and ours is that, in a representative sample, female workers are more likely to be unemployed or face wage cuts during the pandemic since many of them are working in the service industry. In contrast, workers in our analysis are from large industrial companies with relatively higher education and are more likely to be engaged in technical or professional jobs; therefore, they have less fear of job security or wages. We also speculate that women can do more of their tasks from home, especially those with school-age children, because they are more likely to be assigned routine or more narrowly defined standardized tasks with less coordination needs, which is different from Adams-Prassl et al. [ 21 ] who find that women can do fewer tasks from home even within occupations and industries in the US and UK. Using personnel records from a large Japanese manufacturing company, Sato et al. [ 40 ] show that there is a substantial gender difference in developmental job assignment, which presumably comes from different time preferences/constraints between men and women. If female employees can work from home more easily, they will not necessarily be more negatively affected by WFH despite their additional burden in family care during the pandemic.

5. Concluding remarks

Using unique data retrieved from our original survey conducted in cooperation with four manufacturing companies in Japan, we investigated the determinants of the quality of WFH under the COVID-19 pandemic. Specifically, we examined the within-company and within-occupation productivity effects of WFH on employees’ productivity and mental health. Focusing on specific companies also allowed us to exclude the differences in productivity among firms.

We present four findings. First, we confirmed that frequent WFH is associated with decreased productivity. In our interpretation, most workers experienced declines in productivity, probably due to their inadequate preparation for WFH under the sudden shock of the pandemic.

Second, to confirm our interpretation, we identified the possible factors of productivity losses during pandemic-driven WFH. Our estimation results suggest that the major contributors to deteriorations in productivity are poor WFH setups and poor communication at the workplace and with clients. These results imply that companies may enhance employees’ productivity by investing in their WFH setups at home and communication tools.

Third, we also examined the heterogeneity across types of jobs. We categorized occupational categories into four functional roles, i.e., corporate, sales, R&D, and production. We have found that poor WFH setups are one of the major causes of productivity losses across the four occupation types. However, there are also several important causes that are specific to certain occupations. For corporate jobs and sales jobs, poor workplace communication and poor communication with clients seem to be the most crucial. For sales and R&D jobs, the lack of access to crucial information and exclusive equipment appear to contribute to productivity losses. Our findings provide managerial implications that are useful for designing desirable investments to improve employees’ productivity while WFH.

Fourth, our results show that WFH is associated with better employee mental health. Our regression results suggest that workers benefit from a greater focus on work with a quieter environment, less fatigue, and additional time for sleep and rest as a result of the time saved by cutting commuting time. The positive association between WFH and mental health, which is not in line with some early works on the effect of COVID-19 on mental health, may come from two factors: (1) the movement and social life were less restricted in Japan during the pandemic; and (2) WFH was not mandated so that only organizations that can allow and provide support for WFH actually implemented it. Since a lack of time series information on mental health prevents us from ruling out a time-invariant component of employees’ mental status, however, the findings here should be handled with reservations. Nonetheless, while more emphasis tends to be placed on the drawbacks of WFH, our result may suggest that WFH may improve productivity by improving employees’ health and well-being. To that end, let us introduce the answers to the question regarding WFH used in the Company A surveys. The question asked, “ (a)fter the situation returns to normal , how often do you prefer to work from home ? ” Among 1,381 employees who worked from home, only 7.2% answered “none,” while 52.3% and 22.0% answered “1–2 days per week” and “3 days or more per week”, respectively. These results suggest that these workers might have realized the advantages of WFH, and they are in line with the results of Eurofound’s questionnaire survey [ 2 ] conducted with workers in EU member states. The WFH style may take root around the world as a new working style.

Under these circumstances, companies should not dismiss remote working out of hand as a work arrangement option because of lower productivity compared with in-office work. Rather, they need to conduct a detailed analysis of the causes of the productivity gap, make the infrastructure improvements that are necessary for increasing WFH productivity, and send a clear message from top management that WFH can be a productivity booster. Such changes will create opportunities for people who have been unable to work full-time or work as regular employees—that is, employees who are supposed to be willing to make business trips or accept workplace transfers—because of time constraints resulting from life circumstances, such as having to raise children or care for elderly individuals or individuals suffering from illness or a disability. In a way, WFH may be an option that can be used to take full advantage of the workforce’s talents that could be wasted without such arrangement.

Supporting information

S1 table. descriptive statistics..

https://doi.org/10.1371/journal.pone.0261761.s001

Acknowledgments

This study was conducted as joint research under the “Research on Working-style Reform, Health and Productivity Management” (Kuroda) and “Productivity Effect of HRM Policies and Changing Employment Systems” (Owan) projects undertaken at the Research Institute of Economy, Trade and Industry (RIETI). The authors are grateful for helpful comments and suggestions given from Arata Ito, Masayuki Morikawa, Kotaro Tsuru, Makoto Yano, and participants at the RIETI discussion paper seminar.

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• 13. OECD, "Productivity gains from teleworking in the post COVID-19 era: How can public policies make it happen?," OECD, Paris, 2020.

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The impact of work-from-home on employee performance and productivity: a systematic review.

1. Introduction

2.1. study-selection strategy, 2.2. study design inclusion and exclusion criteria, 2.3. quality of assessment, 2.4. synthesis, 3.1. study characteristics, 3.1.1. location, 3.1.2. aims of the study, 3.2. study methodology, 3.2.1. study sample, 3.2.2. nature of the study and design, 3.3. findings, 4. discussion, 5. conclusions, 6. limitations and future research, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Anakpo, G.; Nqwayibana, Z.; Mishi, S. The Impact of Work-from-Home on Employee Performance and Productivity: A Systematic Review. Sustainability 2023 , 15 , 4529. https://doi.org/10.3390/su15054529

Anakpo G, Nqwayibana Z, Mishi S. The Impact of Work-from-Home on Employee Performance and Productivity: A Systematic Review. Sustainability . 2023; 15(5):4529. https://doi.org/10.3390/su15054529

Anakpo, Godfred, Zanele Nqwayibana, and Syden Mishi. 2023. "The Impact of Work-from-Home on Employee Performance and Productivity: A Systematic Review" Sustainability 15, no. 5: 4529. https://doi.org/10.3390/su15054529

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The Realities of Remote Work

• Laura Amico

Work-life boundaries are blurring and managers worry about productivity. What can be done?

The Covid-19 pandemic sparked what economist Nicholas Bloom calls the “ working-from-home economy .” While some workers may have had flexibility to work remotely before the pandemic, this unprecedented shift to remote work looks like it could be here to stay in some form.

• Laura Amico is a former senior editor at Harvard Business Review.

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How Companies Benefit When Employees Work Remotely

Companies that let their workers decide where and when to do their jobs—whether in another city or in the middle of the night—increase employee productivity, reduce turnover, and lower organizational costs, new research suggests.

Prithwiraj Choudhury , an associate professor in the Technology and Operations Management Unit at Harvard Business School, and fellow researchers compared the outcomes of flexible work arrangements at the US Patent and Trademark Office (USPTO). The team found that employees with liberal “work from anywhere” arrangements, similar to those offered at Akamai, NASA, and Github, among others, were 4.4 percent more productive than those following a more traditional “work-from-home” policy that gives schedule flexibility but requires workers to live near the office.

“While prior academic research has studied productivity effects of ‘working from home’ that gives workers temporal flexibility, ‘work from anywhere’ goes a step further and provides both temporal and geographic flexibility,” says Choudhury, who co-authored the paper, (Live and) Work from Anywhere: Geographic Flexibility and Productivity Effects at the United States Patent Office, with HBS doctoral student Cirrus Foroughi and Barbara Larson , executive professor of management at Northeastern University.

While digital technology has made workers more efficient and accessible than ever before, many companies have been slow to let employees work from home regularly, let alone from anywhere at any time. The study’s findings can help firms understand the effects of various flex-work options, and support certain types of employees as they negotiate with employers. Choudhury says the results have important implications for workers, who could potentially move to lower-cost areas, reduce commuting costs, and live closer to family and friends.

Isolating the benefits of remote work

To study the productivity effects of work-from-anywhere policies, Choudhury looked for a setting that would allow the researchers to isolate productivity changes among workers with similar job functions under different remote-work conditions. The USPTO provided the perfect opportunity.

Seeking to increase efficiency, the agency implemented the Telework Enhancement Act Pilot Program (TEAPP) in 2012. The program transitioned patent examiners to a work-from-anywhere policy over 24 months, shifting new examiners each month based on union-negotiated quotas. This implementation process enabled Choudhury and his co-authors to avoid what is known as the selection problem in social science research.

“The concern is that there is some underlying characteristic of people that is driving whether one wants to become a remote worker, and that characteristic is also correlated to productivity,” explains Choudhury.

Prior to TEAPP, examiners could work from home as long as they were within 50 miles of the USPTO headquarters in Alexandria, Virginia, but they had to report to the office once a week. The agency eventually allowed them to work beyond 50 miles away, but still required weekly trips to the office. TEAPP provided full autonomy.

Choudhury and his coauthors compared 600 examiners’ productivity under these various conditions. While working remotely, productivity increased among all examiners and continued to rise with each step toward the full work-from-anywhere policy, the researchers found. Productivity increased 4.4 percent when employees moved from working at home on a limited basis to the location of their choice. Based on a patent's average value, this productivity gain could add $1.3 billion of value to the US economy each year, the researchers estimate.

Many of the examiners also benefited financially by bringing their Greater Washington, DC, salaries to less costly regions, effectively increasing their real incomes. Early- and mid-career workers tended to choose locations based on future career considerations, while workers with longer tenures flocked to “retirement-friendly” destinations, such as Florida.

Work from anywhere isn’t for everyone

To put their findings in perspective and offer a framework for future research, the researchers emphasized the nature of a patent examiner’s work, which requires little coordination with co-workers on a daily basis. Examiners perform their work independently, adhering to the same best practices of patent searches—a style of work that prior research has termed “pooled interdependence.” Choudhury stresses that the research results apply only to companies or units that employ this type of worker.

“For the vast majority of such employers, remote work is a win-win, because the employee can move to a location of choice and save money in cost of living, and the employer will see higher productivity and lower attrition, and save on real estate costs,” says Choudhury.

Choudhury and his fellow researchers contrast pooled interdependence with “reciprocal interdependence,” which requires continued interaction between co-workers, and “sequential interdependence,” which involves a series of tasks performed by different employees.

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Employees with jobs that require minimal coordination could potentially use these findings in negotiations with a prospective employer, says Choudhury. However, work-from-anywhere policies could increase costs in work environments that require brainstorming and project-based interaction, says Choudhury, adding that more research is needed to fully understand the implications of remote work in more collaborative settings.

Looking ahead

As some companies move to adopt broader telecommute policies, others such as Yahoo! have publicly retreated from allowing workers to perform their jobs away from headquarters. Companies have cited the need for office “face time” and the benefits of spontaneous interaction among reasons to avoid remote work, even for highly skilled, autonomous employees. To Choudhury, a deeper factor might underlie their reluctance.

“It’s trust—it’s the fear that people will shirk, and I think it’s the lack of clarity from the academic research as well,” Choudhury says.

The potential for this new research to help inform discussions about remote work policies excites Choudhury. Giving knowledge workers, particularly those who work solitarily, the freedom to choose their location could benefit not only employees, but companies and the environment, too.

“People will gravitate to a location where they want to live, rather than where they have to live,” predicts Choudhury. “This was the big promise of digital technology, that it would allow people to move away from the urban clusters.”

For companies seeking to expand their remote work policies, the USPTO program offers several other considerations for executives:

• Gradual transitions might help employees adjust . The USPTO required in-office examiners to work from home before shifting to the fully autonomous option. A gradual shift might give employees time to set up their own processes for working remotely while they're still close enough to seek their employer's support in person.
• Technology tools can support light supervision . The USPTO's program focused on examiners who worked independently. However, when an employee's work needed a supervisor's review, the agency required employees to use its IT tools to coordinate virtually, bolstering productivity.
• A period of in-office work can provide a strong foundation . Only patent examiners with two years of in-office experience were eligible for the USPTO's remote work programs. These employees had the opportunity to learn from working alongside more experienced examiners before moving away.

About the Author

Kristen Senz is a writer and social media creator for Harvard Business School Working Knowledge. Ailyn Pestana , junior designer and photo coordinator at Harvard Business School, created the charts above. [Image: NoSystem Images ]

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Working from home: Assessing the research evidence

Preliminary recommendations arising from enforced homeworking during the COVID-19 lockdown

The COVID-19-induced lockdown offered unprecedented opportunities for UK employers to trial working from home at scale. This prompted the CIPD to conduct research into the lessons that employers can take from the period of enforced homeworking to improve their flexible working offering in the future.

This eight-month research project aimed to understand the opportunities and challenges from this period of enforced homeworking and to offer recommendations that seek to overcome these challenges and take advantage of the opportunities. The full findings of the research can be found in our report Flexible working: Lessons from the pandemic .

While these findings are based on UK data, the broader trends and implications should be of interest wherever you are based.

What do we hope to learn from this research?

The aim of this research is to:

• Identify the opportunities and challenges presented by widespread homeworking during the COVID-19 pandemic.
• Explore how the COVID-19 pandemic has affected the willingness of employers, and of line managers, to allow or encourage homeworking and other forms of flexible working.

The first phase of this research is a review of existing research on homeworking. The recommendations from this first phase are outlined below and you can also download a copy of the interim report to find out more about these preliminary recommendations.

The next stage of the research involves conducting qualitative and quantitative research with workers in different occupations and settings. The findings from this research are outlined in our report  Flexible working: Lessons from the pandemic .

Preliminary findings 

The first phase of this research identified eight key themes from the review of existing research on homeworking during the COVID-19 lockdown:

• Increased productivity among homeworkers is often achieved through work intensification.
• For some workers, homeworking can provide a more productive environment because there are fewer distractions.
• Knowledge sharing and team relationships often suffer – unless task-related processes are designed to take location into account.
• Innovation can suffer if knowledge sharing and team relationships deteriorate.
• Social isolation can be a problem for some workers, but this depends on personality and lifestyle.
• Avoiding the commute is a major benefit for most. 
• Attention to work-life boundaries is helpful not just for homeworkers but for anyone in the digital age.
• The career downsides are real and need to be managed. 

Preliminary recommendations for employers 

1. Be aware of the differences between ‘standard’ and COVID-enforced homeworking

Employers need to distinguish between homeworking experiences that are specific to the pandemic, and those lessons that can be taken forward into the post-pandemic era. The specific challenges we saw during COVID-enforced homeworking (poor planning, lack of choice, total homeworking and lack of childcare) are not as prevalent during usual homeworking arrangements (and in more usual circumstances) and while employers can learn from these challenges they should not be used to judge the effectiveness of homeworking arrangements. Instead, employers should work with employees on an individual level to understand and overcome any challenges of individual homeworking arrangements and build on any opportunities presented.

2. Homeworking is here to stay – design your working practices to suit all locations

Employers should design work processes that support both homeworkers and conventionally-sited employees, concentrating particularly on knowledge sharing, coordination of work, task-related communications and team relationships to encourage performance and innovation. Work intensification and homeworkers’ career development need to be monitored and managed.

Employers should provide support for homeworkers to manage work-home boundaries and avoid isolation, as well as making the cost-benefit calculations around the ‘hard’ elements of technology and office space.

3. Concentrate on partial, voluntary homeworking as part of designing high quality jobs

The appropriate balance of home and office work depends on the type of work, the team processes in place, the manager’s capability, and the degree of cultural support within the organisation, as well as the individual’s home circumstances and the support the employer can provide for technology and equipment.

As with any kind of flexible working – or indeed any kind of job design – a person-centred approach is most likely to result in a solution that suits the individual, the team and the organisation.

Download the report below

Working from home – Assessing the research evidence

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• How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

Published on October 12, 2022 by Shona McCombes and Tegan George. Revised on November 21, 2023.

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research.

The format of a research proposal varies between fields, but most proposals will contain at least these elements:

Introduction

Literature review.

• Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Table of contents

Research proposal purpose, research proposal examples, research design and methods, contribution to knowledge, research schedule, other interesting articles, frequently asked questions about research proposals.

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal as part of a grad school application , or prior to starting your thesis or dissertation .

In addition to helping you figure out what your research can look like, a proposal can also serve to demonstrate why your project is worth pursuing to a funder, educational institution, or supervisor.

Research proposal length

The length of a research proposal can vary quite a bit. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations or research funding are usually much longer and more detailed. Your supervisor can help you determine the best length for your work.

One trick to get started is to think of your proposal’s structure as a shorter version of your thesis or dissertation , only without the results , conclusion and discussion sections.

Download our research proposal template

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Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We’ve included a few for you below.

• Example research proposal #1: “A Conceptual Framework for Scheduling Constraint Management”
• Example research proposal #2: “Medical Students as Mediators of Change in Tobacco Use”

Like your dissertation or thesis, the proposal will usually have a title page that includes:

• The proposed title of your project
• Your supervisor’s name
• Your institution and department

The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.

Your introduction should:

• Introduce your topic
• Give necessary background and context
• Outline your  problem statement  and research questions

To guide your introduction , include information about:

• Who could have an interest in the topic (e.g., scientists, policymakers)
• How much is already known about the topic
• What is missing from this current knowledge
• What new insights your research will contribute
• Why you believe this research is worth doing

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As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have already done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

• Comparing and contrasting the main theories, methods, and debates
• Examining the strengths and weaknesses of different approaches
• Explaining how will you build on, challenge, or synthesize prior scholarship

Following the literature review, restate your main  objectives . This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

• Improving best practices
• Informing policymaking decisions
• Strengthening a theory or model
• Challenging popular or scientific beliefs
• Creating a basis for future research

Last but not least, your research proposal must include correct citations for every source you have used, compiled in a reference list . To create citations quickly and easily, you can use our free APA citation generator .

Some institutions or funders require a detailed timeline of the project, asking you to forecast what you will do at each stage and how long it may take. While not always required, be sure to check the requirements of your project.

Here’s an example schedule to help you get started. You can also download a template at the button below.

Download our research schedule template

If you are applying for research funding, chances are you will have to include a detailed budget. This shows your estimates of how much each part of your project will cost.

Make sure to check what type of costs the funding body will agree to cover. For each item, include:

• Cost : exactly how much money do you need?
• Justification : why is this cost necessary to complete the research?
• Source : how did you calculate the amount?

To determine your budget, think about:

• Travel costs : do you need to go somewhere to collect your data? How will you get there, and how much time will you need? What will you do there (e.g., interviews, archival research)?
• Materials : do you need access to any tools or technologies?
• Help : do you need to hire any research assistants for the project? What will they do, and how much will you pay them?

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

A PhD, which is short for philosophiae doctor (doctor of philosophy in Latin), is the highest university degree that can be obtained. In a PhD, students spend 3–5 years writing a dissertation , which aims to make a significant, original contribution to current knowledge.

A PhD is intended to prepare students for a career as a researcher, whether that be in academia, the public sector, or the private sector.

A master’s is a 1- or 2-year graduate degree that can prepare you for a variety of careers.

All master’s involve graduate-level coursework. Some are research-intensive and intend to prepare students for further study in a PhD; these usually require their students to write a master’s thesis . Others focus on professional training for a specific career.

Critical thinking refers to the ability to evaluate information and to be aware of biases or assumptions, including your own.

Like information literacy , it involves evaluating arguments, identifying and solving problems in an objective and systematic way, and clearly communicating your ideas.

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

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McCombes, S. & George, T. (2023, November 21). How to Write a Research Proposal | Examples & Templates. Scribbr. Retrieved June 10, 2024, from https://www.scribbr.com/research-process/research-proposal/

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© THE INTERCEPT

ALL RIGHTS RESERVED

Leaked Grant Proposal Details High-Risk Coronavirus Research

The proposal, rejected by U.S. military research agency DARPA, describes the insertion of human-specific cleavage sites into SARS-related bat coronaviruses.

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A grant proposal   written by the U.S.-based nonprofit the EcoHealth Alliance and submitted in 2018 to the Defense Advanced Research Projects Agency, or DARPA, provides evidence that the group was working — or at least planning to work — on several risky areas of research. Among the scientific tasks the group described in its proposal, which was rejected by DARPA, was the creation of full-length infectious clones of bat SARS-related coronaviruses and the insertion of a tiny part of the virus known as a “proteolytic cleavage site” into bat coronaviruses. Of particular interest was a type of cleavage site able to interact with furin, an enzyme expressed in human cells.

The EcoHealth Alliance did not respond to inquiries about the document, despite having answered previous queries from The Intercept about the group’s government-funded coronavirus research. The group’s president, Peter Daszak, acknowledged the public discussion of an unfunded EcoHealth proposal in a tweet on Saturday. He did not dispute its authenticity.

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Since the genetic code of the coronavirus that caused the pandemic was first sequenced, scientists have puzzled over the “furin cleavage site.” This strange feature on the spike protein of the virus had never been seen in SARS-related betacoronaviruses, the class to which SARS-CoV-2, the coronavirus that causes the respiratory illness Covid-19, belongs.

The furin cleavage site enables the virus to more efficiently bind to and release its genetic material into a human cell and is one of the reasons that the virus is so easily transmissible and harmful. But scientists are divided over how this particular site wound up in the virus, and the cleavage site became a major focus of the heated debate over the origins of the pandemic.

Many who believe that the virus that caused the pandemic emerged from a laboratory have pointed out that it is unlikely that the particular sequence of amino acids that make up the furin cleavage site would have occurred naturally.

Adherents of the idea that SARS-CoV-2 emerged from a natural spillover from animal hosts have argued that it could have evolved naturally from an as-yet undiscovered virus. Further, they argued, scientists were unlikely to have engineered the feature.

“There is no logical reason why an engineered virus would utilize such a suboptimal furin cleavage site, which would entail such an unusual and needlessly complex feat of genetic engineering,” 23 scientists wrote earlier this month in an  article in the journal Cell. “There is no evidence of prior research at the [Wuhan Institute of Virology] involving the artificial insertion of complete furin cleavage sites into coronaviruses.”

But the proposal describes the process of looking for novel furin cleavage sites in bat coronaviruses the scientists had sampled and inserting them into the spikes of SARS-related viruses in the laboratory.

“We will introduce appropriate human-specific cleavage sites and evaluate growth potential in [a type of mammalian cell commonly used in microbiology] and HAE cultures,” referring to cells found in the lining of the human airway, the proposal states.

The new proposal, which also described a plan to mass vaccinate bats in caves, does not provide conclusive evidence that the virus that caused the pandemic emerged from a lab. And virus experts remain sharply divided over its origins. But several scientists who work with coronaviruses told The Intercept that they felt that the proposal shifted the terrain of the debate.

Tipping the Scales

“Some kind of threshold has been crossed,” said Alina Chan, a Boston-based scientist and co-author of the upcoming book “Viral: The Search for the Origin of Covid-19.” Chan has been vocal about the need to thoroughly investigate the possibility that SARS-CoV-2 emerged from a lab while remaining open to both possible theories of its development. For Chan, the revelation from the proposal was the description of the insertion of a novel furin cleavage site into bat coronaviruses — something people previously speculated, but had no evidence, may have happened.

“Let’s look at the big picture: A novel SARS coronavirus emerges in Wuhan with a novel cleavage site in it. We now have evidence that, in early 2018, they had pitched inserting novel cleavage sites into novel SARS-related viruses in their lab,” said Chan. “This definitely tips the scales for me. And I think it should do that for many other scientists too.”

Richard Ebright, a molecular biologist at Rutgers University who has espoused the possibility that SARS-CoV-2 may have originated in a lab, agreed. “The relevance of this is that SARS Cov-2, the pandemic virus, is the only virus in its entire genus of SARS-related coronaviruses that contains a fully functional cleavage site at the S1, S2 junction,” said Ebright, referring to the place where two subunits of the spike protein meet. “And here is a proposal from the beginning of 2018, proposing explicitly to engineer that sequence at that position in chimeric lab-generated coronaviruses.”

“A possible transmission chain is now logically consistent — which it was not before I read the proposal.”

Martin Wikelski, a director at the Max Planck Institute of Animal Behavior in Germany, whose work tracking bats and other animals was referenced in the grant application without his knowledge, also said it made him more open to the idea that the pandemic may have its roots in a lab. “The information in the proposal certainly changes my thoughts about a possible origin of SARS-CoV-2,” Wikelski told The Intercept. “In fact, a possible transmission chain is now logically consistent — which it was not before I read the proposal.”

But others insisted that the research posed little or no threat and pointed out that the proposal called for most of the genetic engineering work to be done in North Carolina rather than China. “Given that the work wasn’t funded and wasn’t proposed to take place in Wuhan anyway it’s hard to assess any bearing on the origin of SARS-CoV-2,” Stephen Goldstein, a scientist who studies the evolution of viral genes at the University of Utah, and an author of the recent Cell article, wrote in an email to The Intercept.

Other scientists contacted by The Intercept noted that there is published evidence that the Wuhan Institute of Virology was already engaged in some of the genetic engineering work described in the proposal and that viruses designed in North Carolina could easily be used in China. “The mail is filled with little envelopes with plasmid dried on to filter paper that scientists routinely send each other,” said Jack Nunberg, director of the Montana Biotechnology Center at the University of Montana.

NIH Documents Provide New Evidence U.S. Funded Gain-of-Function Research in Wuhan

Vincent Racaniello, a professor of microbiology and immunology at Columbia University, was adamant that the proposal did not change his opinion that the pandemic was caused by a natural spillover from animals to humans. “There are zero data to support a lab origin ‘notion,’” Racaniello wrote in an email. He said he believed that the research being proposed had the potential to fall in the category of gain-of-function research of concern, as did an experiment that was detailed in another grant proposal recently obtained by The Intercept. The government funds such research, in which scientists intentionally make viruses more pathogenic or transmissible in order to study them, only in a narrow range of circumstances . And DARPA rejected the proposal at least in part because of concerns that it involved such research.

While Racaniello acknowledged that the research in the DARPA proposal entailed some danger, he said “the benefits far, far outweigh the risk.” He also said the fact that the viruses described in the proposal were not known pathogens mitigated the concern. “This is not SARS,” he said, referring to SARS-CoV-1, the virus that caused a 2003 outbreak. “It’s SARS-related.”

But SARS-CoV-2 is not a direct descendant of that virus — it’s a relative.

In fact, the viruses described in the grant proposal, which was first posted online by the research group DRASTIC , were not known pathogens. And the authors of the grant proposal make the case that because the scientists would be using SARS-related bat viruses, as opposed to the SARS virus that was known to infect humans, the research was exempt from “gain-of-function concerns.” But according to several scientists interviewed by The Intercept, the viruses presented a threat nevertheless.

“The work describes generating full-length bat SARS-related coronaviruses that are thought to pose a risk of human spillover. And that’s the type of work that people could plausibly postulate could have led to a lab-associated origin of SARS-CoV-2,” said Jesse Bloom, a professor at Fred Hutchinson Cancer Research Center and director of the Bloom Lab, which studies the evolution of viruses. Bloom pointed out that the scientists acknowledge the risk to humans in their proposal. “It’s an explicit goal of the grant to identify the bat SARS-related coronaviruses that they think pose the highest risk.”

Stuart Newman, a professor of cell biology who directs the developmental biology laboratory at New York Medical College, also said the fact that the viruses weren’t known to be dangerous didn’t preclude the possibility that they might become so. “That’s really disingenuous,” Newman said of the argument. “The people that are claiming natural emergence say that it begins with a bat virus that evolved to be compatible with humans. If you use that logic, then this virus could be a threat because it could also make that transition.” Newman, a longtime critic of gain-of-function research and founder of the Council for Responsible Genetics, said that the proposal confirmed some of his worst fears. “This is not like slightly stepping over the line,” said Newman. “This is doing everything that people say is going to cause a pandemic if you do it.”

While the grant proposal does not provide the smoking gun that SARS-CoV-2 escaped from a lab, for some scientists it adds to the evidence that it might have. “Whether that particular study did or didn’t [lead to the pandemic], it certainly could have,” said Nunberg, of Montana Biotechnology Center. “Once you make an unnatural virus, you’re basically setting it up in an unstable evolutionary place. The virus is going to undergo a whole bunch of changes to try and cope with its imperfections. So who knows what will come of it.” The risks of such research are profound and irreversible, he said. “You can’t call back the virus once you release it into the environment.”

DARPA, a division of the Department of Defense, said regulations prevented it from confirming that it had reviewed the proposal. “Since EcoHealth Alliance may or may not be the direct source of the material in question, and we are precluded by Federal Acquisition Regulations from divulging bidders or any associated proposal details, we recommend that you reach out to them to confirm the document’s authenticity,” a DARPA spokesperson wrote in an email to The Intercept. The British Daily Telegraph reported that it had confirmed the document’s legitimacy with a former member of the Trump administration.

The Telegraph story erroneously reported that the scientists proposed to inoculate bats with live viruses. In fact, they hoped to inoculate them with chimeric S proteins, which were proposed to be developed through a subcontract in the grant in Ralph Baric’s lab at the University of North Carolina at Chapel Hill, not in Wuhan. Baric did not respond to The Intercept’s request for comment.

Conflict of Interest

Many questions remain about the proposal, including whether any of the research described in it was completed. Even without the DARPA funding, there were many other potential ways to pay for the experiments. And scientists interviewed for this article agreed that often researchers do some of the science they describe in proposals before or after they submit them.

“This was a highly funded group of researchers that wouldn’t let one rejection halt their work,” said Chan, the “Viral” author.

Perhaps the most troubling question about the proposal is why, within the small group of scientists who have been searching for information that could shed light on the origins of the pandemic, there has apparently been so little awareness of the planned work until now. Peter Daszak and Linfa Wang, two of the researchers who submitted the proposal, did not previously acknowledge it.

Daszak, the EcoHealth Alliance president, has actively sought to quash interest in the idea that the novel coronavirus originated in a lab. In February 2020, as the pandemic began to grip major cities in the U.S., he began organizing scientists to write an open letter that was published in the Lancet addressing the origins of the virus. “The rapid, open, and transparent sharing of data on this outbreak is now being threatened by rumours and misinformation around its origins,” read the statement signed by Daszak and 26 co-authors. “We stand together to strongly condemn conspiracy theories suggesting that COVID-19 does not have a natural origin.”

Daszak directed and gathered signatures for the letter, all the while suggesting that he and his collaborators on the proposed DARPA project, Baric and Wang, distance themselves from the effort.

“I spoke with Linfa [Wang] last night about the statement we sent round. He thinks, and I agree with him, that you, me and him should not sign this statement, so it has some distance from us and therefore doesn’t work in a counterproductive way,” Daszak wrote to Baric in February 2020, just weeks before it appeared in the journal, according to an email surfaced a year later by public health investigative research group U.S. Right to Know. “We’ll then put it out in a way that doesn’t link it back to our collaboration so we maximize an independent voice.” Ultimately, Daszak did sign the letter.

“I also think this is a good decision,” Baric replied. “Otherwise it looks self-serving and we lose impact.”

Baric and Wang — a professor in the emerging infectious diseases program at Duke-NUS Medical School, Singapore — did not respond to inquiries from The Intercept about their decision not to sign the letter in the Lancet.

Daszak was also a member of the joint team the World Health Organization sent to China in February 2020 to investigate the origins of the pandemic, which concluded that it was “extremely unlikely” that the virus had been released from a laboratory. (In March, WHO  called  for further investigation of the origins of the virus and stated that “all hypotheses remain open.”)

“I find it really disappointing that one of the members of the joint WHO-China team, which is essentially the group of scientists that were tasked as representatives of both the scientific community and the World Health Organization of investigating this, are actually on this proposal, knew that this line of research was at least under consideration, and didn’t mention it all,” said Bloom, of Fred Hutch. “Whatever information that relates to help people think about this just needs to be made transparently available and explained.”

Correction: September 24, 2021

A previous version of this article stated incorrectly that the EcoHealth Alliance proposal had been featured on Sky News Australia.

Correction: September 23, 2021, 3:30 p.m.

A previous version of this article stated incorrectly that Linfa Wang was a member of the WHO-China team.

Contact the author:

Additional credits:.

Additional Reporting: Mara Hvistendahl

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Platforms like Bonsai, Proposify, Trello, Asana, and FreshBooks allow freelancers to organize themselves, as well as to create and track proposals. They even come with analytics, which can be helpful (or hurtful, depending on how well your business is going). I don’t need data to tell you that automating something makes it faster, though, so give these tools a try.

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After submitting a proposal, follow up within a week if you haven’t heard back. This feels annoying (because it is), but annoying people get contracts. If your proposal is rejected, see if they’ll tell you why. They might not, but if they do, it’s a chance to improve your proposal-writing skill for next time. The art of proposal writing is not static; continuously seek out resources to improve your skills—either from potential clients, other freelancers, or professional organizations. You got this.

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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 2024 Economic Report of the   President

Today, the Council of Economic Advisers under the leadership of Chair Jared Bernstein released the 2024 Economic Report of the President , the 78 th report since the establishment of CEA in 1946. The 2024 Report brings economic evidence and data to bear on many of today’s most significant issues and questions in domestic and international economic policy:

Chapter 1, The Benefits of Full Employment , which is dedicated to the late Dr. William Spriggs, examines the labor market, distributional, and macroeconomic impacts of full employment, with a particular focus on the benefits for economically vulnerable groups of workers who are much more likely to be left behind in periods of weak labor markets.

Chapter 2, The Year in Review and the Years Ahead , describes macroeconomic and financial market trends in 2023 and presents the Federal government’s FY 2024 macroeconomic forecast.

Chapter 3, Population, Aging, and the Economy , explains how long-run trends in fertility and mortality are shaping the U.S. population and labor force.

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Chapter 7, An Economic Framework for Understanding Artificial Intelligence , uses an economic framework to explore when, how, and why AI may be adopted, adapting standard economic models to explore AI’s potential effects on labor markets, while examining policy decisions that will affect social and macroeconomic outcomes.

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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