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Workplace Stress and Productivity: A Cross-Sectional Study

1 University of Oklahoma at Tulsa, Tulsa, OK

Rosey Zackula

2 Office of Research, University of Kansas School of Medicine-Wichita, Wichita, KS

Katelyn Dugan

3 Department of Population Health, University of Kansas School of Medicine-Wichita, Wichita, KS

Elizabeth Ablah

Introduction.

The primary purpose of this study was to evaluate the association between workplace stress and productivity among employees from worksites participating in a WorkWell KS Well-Being workshop and assess any differences by sex and race.

A multi-site, cross-sectional study was conducted to survey employees across four worksites participating in a WorkWell KS Well Being workshop to assess levels of stress and productivity. Stress was measured by the Perceived Stress Scale (PSS) and productivity was measured by the Health and Work Questionnaire (HWQ). Pearson correlations were conducted to measure the association between stress and productivity scores. T-tests evaluated differences in scores by sex and race.

Of the 186 participants who completed the survey, most reported being white (94%), female (85%), married (80%), and having a college degree (74%). A significant inverse relationship was observed between the scores for PSS and HWQ, r = −0.35, p < 0.001; as stress increased, productivity appeared to decrease. Another notable inverse relationship was PSS with Work Satisfaction subscale, r =−0.61, p < 0.001. One difference was observed by sex; males scored significantly higher on the HWQ Supervisor Relations subscale compared with females, 8.4 (SD 2.1) vs. 6.9 (SD 2.7), respectively, p = 0.005.

Conclusions

Scores from PSS and the HWQ appeared to be inversely correlated; higher stress scores were associated significantly with lower productivity scores. This negative association was observed for all HWQ subscales, but was especially strong for work satisfaction. This study also suggested that males may have better supervisor relations compared with females, although no differences between sexes were observed by perceived levels of stress.

INTRODUCTION

Psychological well-being, which is influenced by stressors in the workplace, has been identified as the biggest predictor of self-assessed employee productivity. 1 The relationship between stress and productivity suggests that greater stress correlates with less employee productivity. 1 , 2 However, few studies have examined productivity at a worksite in relation to stress.

Previous research focused on burnout, job satisfaction, or psychosocial factors and their association with productivity; 3 – 7 all highlight the importance of examining overall stress on productivity. Other studies focused on self-perceived stress and employer-evaluated job performance instead of self-assessed productivity. 8 However, most studies examining this relationship have been occupation specific. 8 , 9 Larger studies examining this relationship were performed in other countries. 1 , 5 , 9 , 10

The purpose of this study was twofold. First, the study sought to elucidate the relationship between stress and productivity in four worksites in Kansas. Second, the study sought to examine potential differences in stress and productivity by sex and race.

Recruitment and Sampling Procedures

The target population was employees from four WorkWell KS worksites. WorkWell KS is a statewide worksite initiative in Kansas that provides leadership and resources for businesses and organizations to support worksite health. Because access to employee emails was unavailable, a URL link to an online survey was sent to the worksite contact, who was responsible for ensuring the distribution of the URL link to a cross-section of employees at the worksite. Following a WorkWell KS workshop (held in Topeka, Kansas on November 6, 2017) attendees from the four worksites were recruited to distribute a link to an online survey to their employees. Workshop attendees were members of wellness committees or were worksite representatives. Employee responses to the online survey were collected through mid-December 2017. No compensation was given for disseminating the survey link or for participating in the study. This study was approved by the University of Kansas School of Medicine-Wichita’s Human Subjects Committee.

Online Survey

The online survey comprised demographic items with two instruments, the Perceived Stress Scale (PSS), 11 and the Health and Work Questionnaire (HWQ). 12 Demographic items included employee, sex, race, age, marital status, and highest level of education completed.

Perceived Stress Scale

Stress was measured by the PSS, a 10-item questionnaire designed for use in community samples. The purpose of the instrument is to assess global perceived stress during the past month. Each item is measured with a Likert-type scale (0 = Never, 1 = Almost Never, 2 = Sometimes, 3 = Fairly Often, 4 = Very Often). This scale is reversed on four positively stated questions. Scoring of the PSS is obtained by summing all responses. Results range from zero to 40, with higher PSS scores indicating elevated stress: scores of 0 – 13 are considered low stress, 14 – 26 moderate stress, and 27 – 40 are high perceived stress. The results for perceived stress were used by this study as an indication of psychological well-being.

Health and Work Questionnaire

The HWQ is a 24-item instrument that measures multidimensional worksite productivity. Productivity is assessed by asking respondents how they would describe their efficiency, overall quality of work, or overall amount of work in one week. All items are scaled with Likert-type response anchors, each ranging from 1 to 10 points. Most are positively worded items with response scales from least (scored as a 1) to most favorable (scored as a 10). Exceptions are items 1 and 16 through 24, which are negatively worded and reversed scored. Items are divided into six sub-scales: productivity, concentration/focus, supervisor relations, non-work satisfaction, work satisfaction, and impatience/irritability. As part of the HWQ, employees assessed productivity two ways: on themselves and how their supervisor or co-workers might perceive it. Accordingly, productivity is stratified into a self-assessed sub-score and perceived other-assessed sub-score. HWQ scores are tallied and averaged for each sub-scale, with higher scores generally indicating greater productivity.

The Consent Process

Representatives who participated in the WorkWell KS workshop sent an e-mail to their employees with a request to click on the link and complete the online survey. The link opened the electronic consent, which was the opening remark, followed by the two assessment instruments and the demographic items. Consent was implied by participation in the survey. To encourage survey participation, representatives also sent employees a few e-mail reminders at their own discretion.

Statistical Analysis

The statistical analysis included descriptive statistics, measures of association, and comparisons of survey responses by sex and race. Descriptive statistics comprised response summaries; means and standard deviations were used for continuous variables, while frequency and percentages were used for categorical responses. The relationship between stress and productivity measures were assessed using Pearson correlations. Sex and race comparisons for PSS and HWQ subscales were evaluated using two-sided t-tests; alpha was set at 0.05 as the level of significance. Study participants with missing values were excluded pairwise from the analysis.

Response Rates

Four of nine worksites participated in the study, including two health departments (89 participants), one school district (76 participants), and one non-profit for the medically underserved (21 participants). A total of 188 employees opened the survey link, 186 employees answered the first question of the survey, and 174 employees completed the survey items. The 12 study participants with missing values were excluded from the pairwise analysis. The response rate, defined as those participants who completed the survey, was 58.6% (n = 174). To protect the confidentiality of respondents, data were aggregated and no other comparisons were made by location.

Participants who completed the survey included 174 employees from four worksites in Kansas. Of those who responded, 94% (155 out of 165) reported being white, 85% (142 of 167) reported being female, 81% (124 of 153) reported being between 30 and 59 years, and 60% (99 of 166) reported having a bachelor’s degree or higher ( Table 1 ).

Participant demographics.

With regard to measures of stress, the mean PSS was 16.4, with a standard deviation of 6.2, suggesting that employees have moderate levels of stress at these locations. This result was consistent with the HWQ question regarding “overall stress felt this week”, with a mean score of 4.7 (SD 2.5; 10 is “very stressed”). Regarding measures of productivity, the mean overall HWQ was 6.3 (SD 0.7). With the exception of reverse items, as noted below, scores of 10 indicated high levels of productivity. Mean scores by scale were: 7.3 (SD 1.0) for overall productivity, with 7.5 (SD 1.3) for own assessment, and 7.5 (SD 1.2) for perceived other’s assessment; 7.1 (SD 2.7) supervisor relations, 7.8 (SD 1.8) for non-work satisfaction, and 7.3 (SD 1.7) for work satisfaction. The mean scale for the reverse items scores were concentration/focus at 3.4 (SD 2.0), and impatience/irritability 3.2 (SD 1.6).

Correlations between the PSS and the HWQ subscales ranged from −0.61 to 0.55 ( Table 2 ). A negative association was observed between the PSS and the overall HWQ, r(177) = − 0.35, p < 0.001. While each of the positively-coded HWQ subscales was associated negatively with the PSS, the strongest correlation occurred between work satisfaction and PSS, r(177) = −0.61, p < 0.001, suggesting that as stress increases work satisfaction declines.

Measures of correlation within and between the PSS and HWQ.

HWQ: Health and Work Questionnaire mean score; PSS: Perceived Stress Scale mean score

In evaluating differences by sex, mean scores were significantly higher for males compared with females for the HWQ Supervisor Relations subscale (8.4 (SD 2.1) versus 6.9 (SD 2.7), respectively; p < 0.005; Table 3 ). No other sex differences were observed for either instrument. Similarly, there were no significant differences by race.

Comparing results of the PSS and the HWQ by sex.

Findings suggested there is an inverse association between overall stress and productivity; higher PSS scores were associated with lower HWQ scores. These findings are consistent with other cross-sectional studies comparing productivity and other measures of psychological well-being. 1 , 8 , 9 , 10 Thus, employer efforts to decrease stress in the workplace may benefit employee productivity levels.

In addition, males scored higher for supervisor relations in the HWQ than females. This finding may suggest that males have stronger relationships with their supervisors. Indeed, there is compelling evidence to suggest the main factor affecting job satisfaction and performance is the relationship between supervisors and employees. 13 Although, this relationship may be mitigated by employee-supervisor interactions of sex, race/ethnicity, status, education, age, support systems, and other factors, none of which were evaluated in the current study.

For example, Rivera-Torres et al. 14 suggested that women with support systems, defined as co-workers and supervisors, experienced less work stress than males. Results from this study seemed to support Rivera-Torres et al. 14 in that females tended to report higher levels of stress compared with males (although not significant) and reported weaker relationships with their supervisors. In addition, Peterson 15 evaluated what employee’s value at work and found that males and females differed significantly. When asked to rank work values, men valued pay/money/benefits along with results/achievement/success most, whereas women valued friends/relationships along with recognition/respect. Perhaps, more research is necessary to understand the nuances between co-worker and supervisor regarding work satisfaction and productivity.

The study contributes to the literature in the use of different metrics for psychological well-being, defined as stress. Multiple organizations within Kansas were evaluated for both productivity and stress. To our knowledge, the PSS and HWQ have never been used together to measure the relationship between stress and productivity. Results suggested that overall productivity (HWQ) was associated with the HWQ “work satisfaction” subscale. Perceived stress also had the strongest inverse relationship with HWQ sub-scale “work satisfaction” when compared with HWQ sub-scale “productivity”.

This study suggested that productivity, stress, and job satisfaction were correlated, therefore, additional research needs to include each of these variables in greater detail as the current literature has been mixed on their relationships and potential collinearity. For example, one study examining two occupations suggested psychological well-being (defined as psychological functioning) was associated with productivity, whereas job satisfaction did not. 7 In contrast, another study suggested that psychological well-being has been a bigger factor in job productivity than work satisfaction alone, but both are associated with job productivity. 9 This current study was able to examine this relationship by using the PSS and the HWQ together.

More research is needed to understand these differences by standardizing terminology. In this study, psychological well-being was defined as stress. However, other studies have defined psychological well-being as happiness or as one’s psychological functioning. 7 , 8 This study also expanded the relationship between psychological well-being and stress. Previous research focused more on the relationship between productivity and burnout or job satisfaction.

This study had limitations such as a small sample size (in number of organizations and number of employees). The sample size assessed small organizations in the United States, whereas many other large scale studies on stress occurred over multiple large organizations in other countries. 1 , 10 There was limited racial diversity in the current study, as 6.1% (10 of 165) reported being non-white. The population studied was also primarily female, limiting the strength of comparisons made between sexes. Furthermore, because worksites often share computers, questionnaires may have been completed using the same IP address; thus, we were unable to prevent multiple entries from the same individual.

The current study did not detect a difference in productivity or stress by race. This differed from other research. For instance, non-whites experience greater overall stress than whites potentially attributable to poorer employment status, income, and education. 16 Non-whites experience stress secondary to racial discrimination. 17 , 18 In one study, when examining productivity among university faculty, non-whites reported greater stress and produced less research (productivity) compared to whites. 16 Further research needs to be conducted on productivity and stress by race and ethnicity, and associated variables, such as employment status, income, education, and occupation, need to be accounted for in analysis. Differences between other research and the current study regarding race may be attributed to the fact that only 6% of respondents who answered race reported being non-white, making racial diversity in this study limited, although representative of the population sampled.

CONCLUSIONS

This study suggested there is a negative correlation between overall stress and productivity: higher stress scores were significantly associated with lower productivity scores. This negative association was observed for all HWQ subscales, but was especially strong for work satisfaction. This study also suggested that males may have better supervisor relations compared to females, although no differences between sexes were observed by perceived levels of stress. There was no difference in productivity or stress by race. The results of this study suggested that employer efforts to decrease employee stress in the workplace may increase employee productivity.

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Stress at the Workplace and Its Impacts on Productivity: A Systematic Review from Industrial Engineering, Management, and Medical Perspective

2022, Industrial Engineering & Management Systems

In every fast-paced surrounding, stress is present in every life aspect, including at the workplace. It is a deeply personal experience, with various stressors affecting every individual differently. This study assessed the past and present workplace stress-related information and analyzed its impact on productivity. It primarily concentrates on the field's philosophical principles, while providing a collection of directions for future study as well. This study was formed in the statement of PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis). The impact of stress at the workplace on the employee's productivity was observed in the cohort and cross-sectional studies from the perspective of industrial engineering, management, and medicine. Four eligible studies were qualitatively assessed from 2,642 identified literature through four databases (Cochrane, Science Direct, Scielo, and PubMed) using keywords stress, impact, productivity, industrial engineering, management, and medicine. The study was convinced that stress at the workplace contributes to worsening relationships at home, worsening relationships between superiors and subordinates as well as contracting diseases. It has a potential negative impact on productivity. Furthermore, the work environment plays a significant contribution in inducing workplace stress because of human physiologic response. Noxious stress is detrimental to the human body, especially if maintained in the long run. Therefore, stress management is imperative before it is too late.

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The Graduate College at the University of Illinois at Urbana-Champaign

Productivity, strategies for increasing productivity:.

• Schedule regular work hours
• Begin working promptly at designated times
• Allocate your sufficient time for high priority tasks
• Minimize time allocated to low priority tasks
• Identify ways to sustain motivation
• Work systematically utilizing tasks lists
• Minimize distractions in your workplace
• Obtain a sufficient amount of sleep every night
• Engage in wellness activities
• Hold yourself accountable to a work plan
• Review your work consistently and evaluate factors affecting productivity

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Open Access

Peer-reviewed

Research Article

Does working from home work? That depends on the home

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

* E-mail: [email protected]

Affiliation Department of Finance, School of Business and Economics, Maastricht University, Maastricht, The Netherlands

Contributed equally to this work with: Piet Eichholtz, Nils Kok

Roles Conceptualization, Writing – review & editing

Roles Conceptualization, Data curation, Methodology, Supervision, Writing – review & editing

• Martijn Stroom, 
• Piet Eichholtz, 

• Published: August 7, 2024
• https://doi.org/10.1371/journal.pone.0306475
• Peer Review
• Reader Comments

Working from home (WFH) has risen in popularity since the COVID-19 pandemic. There is an ongoing debate about the productivity implications of WFH, but the physical climate of the home office has received only limited attention. This paper investigates the effect of home office satisfaction and environment-improving behavior on productivity and burnout tendency for WFH employees. We surveyed over 1,000 Dutch WFH individuals about their home office and perceived WFH performance. We fit logistic regressions and structural equation models to investigate the effect of home office satisfaction and characteristics on self-reported productivity, burnout tendency, and willingness to continue WFH. Our results reveal that individual differences in WFH productivity are explained by heterogeneity in the physical home office environment. Higher satisfaction with home office factors is significantly associated with increased productivity and decreased burnout tendency. We continue by showing that more ventilation during working hours is associated with increased productivity, willingness to continue WFH, and burnout resilience. This effect is fully mediated by satisfaction with the home office. We find that higher home office satisfaction is associated with WFH success and air-quality-improving behavior is associated with higher satisfaction. Our results underline a holistic perspective such that investing in a healthy and objectively measured physical climate is a key aspect of the bright future of working from home. The move from the work office to the home office needs to be accompanied by careful design and investment in the quality of the office and its climate.

Citation: Stroom M, Eichholtz P, Kok N (2024) Does working from home work? That depends on the home. PLoS ONE 19(8): e0306475. https://doi.org/10.1371/journal.pone.0306475

Editor: Daphne Nicolitsas, University of Crete, GREECE

Received: March 5, 2023; Accepted: June 18, 2024; Published: August 7, 2024

Copyright: © 2024 Stroom 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: All files are available from the OSF database (see https://doi.org/10.17605/OSF.IO/H6J3F ).

Funding: The author(s) received no specific funding for this work.

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

Introduction

The COVID-19 pandemic, in combination with recent technological advancements, has quickly elevated the status of working from home (WFH) from “occasionally” to “the new normal” [ 1 ]. Earlier uncertainty about the quantity and quality of work produced at home had hampered large-scale corporate acceptance [ 2 , 3 ]. However, these doubts were simply overturned by the COVID-19 pandemic, which forced most knowledge-based employees to work online. Negative stigmas that were previously associated with WFH diminished drastically, at least temporarily [ 1 ]. In addition, prior technological complications were quickly overcome following a pandemic-driven surge in technological innovations, such as the advent of Teams and Zoom calls. This involuntary litmus test pushed WFH out of its infancy. However, what has gained limited attention is the physical climate of the home office in which work takes place. This study investigates the relationship between the home office environment, including available hardware (e.g. computer, chair, etc.) but also environmental conditions (e.g. air quality, temperature, etc.) and self-reported measures of work satisfaction, productivity and burnout tendency.

Work from home: Productivity and performance

The rising popularity of WFH has been well-reported: a recent report by buffer.com [ 4 ] among 2,300 employees showed that over 97% would like to continue to work from home, at least partially. Employees are, on average, willing to take a 5% pay cut for 2–3 days of work from home [ 5 ]. Employees working from home report being as productive as they were at the office before the pandemic [ 6 ]. These positive experiences have led to the prediction that, after the pandemic, 20% of all office work will be carried out from home. This continuation of work from home is expected to boost productivity by almost 5%, although largely unobservable by standard measures, as it stems mainly from a reduction in commuting [ 1 ].

Working from home has clear advantages, as well as disadvantages, for both work performance and human health and well-being. Multiple studies show positive effects on job satisfaction and turnover intent [ 7 – 9 ]. Bloom et al. [ 10 ] report that work from home leads to less commuting and fewer distractions. In addition, exhaustion leading to burnout is negatively related to work from home [ 11 ]. Perceived autonomy seems to be one of the main drivers of these positive effects: the degree to which employees can choose a location and time to work, independently of their supervisors, both predict the intensity of working from home, as well as job performance, mental burnout, and job dedication, even during the pandemic [ 10 – 13 ].

More recently, Bloom et al. [ 14 ] found only modest self-reported and realized productivity increases for WFH during COVID-19, whereas others identified productivity decreases for those who did not WFH before the pandemic, suggesting selection bias in previous studies [ 15 ]. Moreover, output assessments among ICT workers suggest productivity actually drops at home [ 16 ]. In the past, the positive relationship between WFH intensity and productivity has repeatedly been found to be non-linear. Golden & Vega [ 17 ] find that the relationship between WFH intensity and productivity is nonlinear, with optimal productivity at 16 WFH hours per week, beyond which job satisfaction and performance decline. A survey by State of the Work in 2022 found that, among 2,000 respondents, 45% think career growth will be at risk with increased WFH [ 18 ]. Unsurprisingly, it is coworkers’ relationships that suffer most from WFH, leading to professional isolation, which in turn has the potential to escalate into decreased performance and increased turnover intent [ 9 ]. Offline or online communication could mitigate these negative effects, but only partially [ 13 , 19 ]. For instance, Yang et al. [ 20 ] find that firm-wide remote work inevitably lowers communication quality, as less communication leads to a worsening of information sharing.

Beyond having implications for coworker relationships, WFH may also bring new interpersonal problems to light. Felstead & Henseke [ 21 ] suggest that homeworkers are burdened by the “social exchange theory”: they work harder, longer, and work unpaid hours in order to justify their freedom to work from a preferred location. Workers thus (over)compensate for the perception that they might work less when not being observed. The resulting work exhaustion may offset the positive effects of WFH on productivity, and may even lead to burnout symptoms [ 22 ]. In addition, research shows that people working from home find it hard to detach from work, disrupting their work-life balance [ 13 , 23 ]. Interestingly, the work-family conflict was previously considered to decrease with WFH, supposedly due to increased autonomy [ 9 ]. The current perception of WFH having a negative impact on work-life balance could therefore also be a pandemic-specific challenge.

Although academic findings on the implications of WFH vary, it is also important that beyond the average effects, substantial heterogeneity has been documented across jobs and individuals. To our knowledge, this heterogeneity has solely been explained by work and personal characteristics. For instance, the degree to which a job is suitable for WFH strongly predicts productivity [ 6 ]. A job previously executed behind a desk (e.g., financial services) is more easily shifted to a home office as compared to a manual, labor-orientated occupation. A heavy workload and the degree of monitoring by supervisors also negatively impact the work effectiveness from home [ 13 ]. Jobs that have high levels of interdependence with colleagues, or are outcome-oriented, suffer when WFH intensity increases [ 24 ]. Overall, limited support and inadequate feedback by the employer mitigate the positive effects of WFH [ 11 , 13 ].

At the individual level, self-discipline seems to be a key factor in explaining the effectiveness of WFH [ 13 ]. The degree to which an individual is able to ignore distractions that are not present at the office is important, especially without the same level of social control by co-workers. Additionally, women seem to suffer more from WFH as compared to men [ 6 ]. Women state their job to be less suitable for WFH in general and the presence of children affects WFH productivity for women more negatively as compared to men [ 25 – 27 ]. Finally, the pandemic showed that young workers seem to appreciate work from home more, and opted for WFH more often as compared to older workers [ 28 ]. These results, however, are not stable per se. Another study shows opposite results, where both women and older workers reported being more productive when WFH [ 29 ].

Work from home: The role of the physical environment

What has gained limited attention in explaining individual differences in WFH satisfaction and productivity is the physical climate in which daily work takes place. The COVID-19 pandemic has led to increased attention to the effect of air quality in indoor spaces on pathogen spreading. Specifically, ventilation has become the spearhead combating the airborne spreading of the COVID-19 virus at public and private indoor gatherings [ 30 , 31 ]. The attention to air quality reinforces an existing trend in which workplace quality is becoming more and more important. In the office, employers aim to facilitate a healthy and comfortable work environment for employees, with the goal of promoting productivity [ 32 – 34 ]. Suboptimal air and light quality, temperature, and noise have all been shown to negatively affect productivity and increase sick building symptoms, such as headaches, in the office [ 35 – 38 ]. Hence, ergonomics, temperature, and noise pollution are all considered by modern employers in order to minimize interference with comfort and wellbeing (and ultimately: productivity) in the office [ 39 ].

For the move to the home office, a trade-off is to be expected. On the one hand, suboptimal ergonomics at home are not as easily mitigated [ 40 ], and workplace professionalism or quality may suffer [ 41 ]. For instance, not having a dedicated office negatively influences productivity at home [ 29 ]. On the other hand, research suggests that controlling the thermostat at home might benefit WFH satisfaction [ 42 , 43 ]. Looking at indoor environmental quality more broadly, Tahmasebi et al. [ 44 ] show that people working at home during the pandemic close their windows more often as compared to before the lockdown. Combined with CO 2 data, they conclude that WFH often leads to worse indoor air quality. Generally, the professionalism or quality of the work environment might suffer, while people’s experienced control over these conditions at home might increase.

To address this knowledge gap, the current study examines the relationship between the home office and work-from-home success. We hypothesize that higher satisfaction with the physical environment in which WFH is being performed is associated with higher perceived productivity and lower burnout tendency. Moreover, in line with the recent research focused on air quality and performance in controlled settings, we hypothesize that improving the home office air quality through ventilation will be associated with higher office satisfaction and subsequent work-from-home outcomes.

Survey participants

We surveyed 1,002 Dutch individuals via the Flycatcher panel. Flycatcher is an academically-orientated research organization that established a high-quality panel representing the Dutch population (for example, see [ 45 – 47 ] for studies using the Flycatcher panel). Flycatcher randomly selected participants from their panel for an online survey, where participation was reimbursed. All Flycatcher participants received written informed consent, were allowed to drop out at any time, and included participants actively consented to participation (‘double-active-opt-in’). For the purpose of our research, we included just office workers (with a minimum age of 18 years old), who worked at least part-time from home at the time of the survey. People without work, previously without work, or working exclusively from the office were excluded from our sample. All data was collected unanimously and thus cannot be traced to an individual in the panel. The research setup was reviewed and approved by Maastricht University’s Ethical Review Committee Inner City Faculties (ERCIC_195_09_06_2020).

Empirical setting

The data collection took place in November 2020. At that time, the Netherlands had been in some form of lockdown for over 8 months due to the COVID-19 pandemic. The government strongly recommended WFH, with the exception of healthcare and other essential workers, and prevented employers from requiring employees to work in person. During this time, employers were not allowed to force their employees to come to the office, and social activities were severely limited. Respondents were asked to answer a selection of questions based on two moments in time: current (working from home) and one year ago (working from the office). Fig 1 provides an overview of the timing of data collection relative to the development of COVID-19 restrictions.

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Timeline of Dutch national COVID-19 policies in 2020, color-coded by restriction intensity. Dark red represents the most stringent restrictions, while light green represents the most liberal policies from a social perspective. Key events include lockdowns, partial lockdowns, and periods of alleviations and restrictions, with specific measures noted at each stage.

https://doi.org/10.1371/journal.pone.0306475.g001

It is relevant to point out that we utilize the COVID-19 restrictions to eliminate selection problems hampering previous research. Before the pandemic-related restrictions, the success and satisfaction of WFH could potentially be explained by self-selection following the request to (voluntarily) move to work from home. Inherent intrinsic motivation, personal characteristics, and ability to adjust to the physical environment could all be omitted factors in that request. From a company perspective, those previously offered the possibility to WFH likely had job characteristics with at least a partial fit with remote work. Due to the pandemic, the susceptibility to selection bias is eliminated, leading to a clean research setting to evaluate the impact of WFH on satisfaction, productivity and burnout.

Material and variable construction

The survey included several previously validated modules. First, in order to measure productivity and work satisfaction, the survey included the Health and Work Questionnaire (see [ 48 – 50 ]. Following a cluster analysis, a revised version was developed, more specifically fitting the working-from-home situation (WFH-HWQ) [ 51 ]. This easily-administered questionnaire allows for the assessment of various factors of work-related health and productivity: productivity, productivity by others, peer relationships, nonwork satisfaction, and stress and irritability.

The survey included several other single-scale estimations of WFH productivity and satisfaction, such as self-reported productivity, satisfaction (with work in general, and with the WFH situation), and happiness. Additionally, participants stated their willingness to continue with WFH. These items were all measured on a 10-point Likert scale, ranging from absolutely not (1) to completely (10). In order to capture the negative spectrum of productivity, a short module measured burnout tendency, comparable to Bloom et al [ 8 ]. Adopted from the Maslach burnout inventory [ 52 ], 6 questions were scored on a 7-point Likert scale, ranging from never (1) to always (7), capturing emotional exhaustion. In addition to these six items, we added a 7-point Likert scale for sick days as well as break time during office hours.

To assess the physical characteristics of the home office, we included two separate modules. The UC Berkeley Center for the Built Environment (CBE) module assesses the perceived indoor environmental quality [ 53 ]. This survey has been extensively used in peer-reviewed research [ 54 , 55 ] and measures satisfaction with all relevant indoor environmental factors, such as indoor temperature, air quality, lighting, and noise. We also included the physical office characteristics available in the CBE module. These factors focus on satisfaction with a variety of attributes in the (home) office, such as desk, chair, screen, hardware, and Wi-Fi satisfaction. All factors are measured on a 7-point Likert scale, ranging from very dissatisfied (1) to very satisfied (7).

In addition to the CBE module on the environment of and hardware in the home office, we included a set of metrics to further assess indoor environmental quality and a variety of job-related measures. The former included layout of the home office (open versus closed), lighting (natural light versus no natural light), and ventilation (none, mechanical systems like HVAC or fans, or manual methods such as opening windows or doors). Additionally, participants were asked to estimate the surface of their home office (length and width in meters), and how often they ventilated their home office (as a percentage of time spent in the home office). Job-related characteristics included the ability of the respondent to perform their work from home (1–10 scale), the company size (1–5, 5–15, 15–50, 50+ employees), length of the workweek in hours, and job category (e.g., governmental, non-governmental, self-employed, or on-call).

Finally, demographic information included age, gender, income, family size, household situation, and housing characteristics. The household situation could support or hamper productivity as compared to the office situation. The house that respondents reside in could interfere with the perceived quality of WFH office characteristics. We therefore match respondent data, based on 4-digit postcode, to data on average urbanicity (‘stedelijkheid’; STED), address-density (‘omgevingsadressendichtheid’; OAD), and house value (‘waarde van onroerende zaken’; WOZ).

Empirical model

Linear regression models..

Model 2 shows the combined model including both the effect of home office hardware and home office indoor environment on our dependent variable y i .

This model also adds physical characteristics of the home office as controls ( OC i ), including lighting, means of ventilation, and the room plan. In the Supporting information, an additional model is shown, in which we match our participants at postcode level to average house characteristics. Running model 2 with and without home office controls, we estimate four models in total for both productivity and burnout tendency.

For all models, we standardized continuous variables, since they are originally measured on different Likert scales, to simplify the interpretation of the coefficients (coefficients are standardized unless specifically mentioned otherwise). As a result, the coefficients are z-scores and must be interpreted such that each coefficient indicates the change in the dependent variable for each standard deviation increase of the independent variable. Upon inspection, S2 Table shows that both desk and chair, as well as screen and the hardware factor, have a correlation ( r ) exceeding 0.70. Since correlations between these variables are not surprising, they can be specified as a combined variable. Thus, for any further analysis, the scores on these two pairs are combined and averaged per participant.

Structural equation model.

Following the main analysis, we implement a mediation analysis, using structural equation modelling (While our study employs a cross-sectional design, which limits the ability to establish causality compared to a cross-lagged design, extensive research from controlled experimental settings supports the relationship between ventilation and air quality and their effects on satisfaction, well-being, and performance. Nevertheless, we remain cautious and consider our assessment an analysis of direct and indirect associations. For more on this, see the Limitations section). This analysis of direct and indirect associations assesses the impact of the physical environment on productivity, mediated by hardware and indoor environment satisfaction factors. For the analysis, we construct two latent variables, ‘Office Hardware’ and ‘Office Indoor Environment’ which each consists of all individual hardware and indoor environment satisfaction variables (see S1 Fig for the loadings per latent variable). The factors are loaded by the marker variable identification approach. By doing so, the estimators of the latent variables on the dependent variable are fixed on the original 7-point satisfaction. In other words, the estimators indicate the effect per point estimate increase on a 7-point scale identical to the scales of the underlying variables (Following model specification analysis, we find strong covariance between the latent variables ‘Office Hardware’ and ‘Office Indoor Environment’, and indicator items desk and chair as well as screen and hardware. Since the correlations between these variables are intuitively not surprising, they can be specified in a saturated model. This saturated model, containing additional parameters estimating those correlations, indeed fits the data better than the restricted model with these correlations fixed to zero (chi-squared difference = 568, DF difference = 3; p < .000; note that we do not combine the pairs desk & chair and screen & hardware pre-analysis in contrast to the multivariate regression, but enter them individually whilst declaring covariance in the SEM model. Doing so increases the Cronbach alpha of both models with 0.05 and improves the overall model fit).

Descriptive findings

Demographics..

The survey was completed by 1,002 participants of which 58.1% are male, with mean age of 43.89 (SD = 12,54). All participants had work that was at least partially executed from home, with 57.9% of the respondents exclusively working from home. Table 1 shows further demographic characteristics. 54.6% of our sample completed higher education (as compared to just over 40% for the Netherlands more broadly in 2019 [ 56 ]) and 53.6% earn more than the median income in the Netherlands. These metrics support the notion that cognitively demanding (desk) jobs are more likely to be suitable to be performed from home [ 6 ]. Considering the home office, we find that they are relatively spacious (M = 25.1 m 2 , SD = 17.4) and predominantly illuminated by natural light (82.6%). Note that we use the estimated length and width of the office (in meters) to calculate the total surface in m 2 . Extreme values (potential mistakes) for either metric ultimately led to unrealistic outliers. As a result, we truncated the office surface from 2 to 100 m 2 (46 data points are excluded).

https://doi.org/10.1371/journal.pone.0306475.t001

Home versus work: Performance differences.

Table 2 shows the general scoring on the main variables of interest, comparing the home office situation with the office by applying nonparametric Wilcoxon signed-rank tests on paired samples’ median differences. For example, the average WFH-HWQ factor productivity score at home is 6.84 out of 10 (SD = 1.28 with a maximum of 9.90). Compared to the office, the WFH-HWQ factor productivity scores higher at work ( p < .001), whereas self-reported productivity does not differ ( p >.06). The overall trend for the other WFH-HWQ factors (excluding Stress) shows a higher score for the office. The single-question estimations of productivity and satisfaction show a slightly higher, yet similar, trend. Since S1 Table shows that the WFH-HWQ factor productivity estimator is strongly correlated with its single-question counterpart ( r = .73, p < .0001), we solely refer to the WFH-HWQ productivity factor when we discuss productivity scores.

https://doi.org/10.1371/journal.pone.0306475.t002

The average burnout score suggests that most of the respondents show limited signs of burnout while in the home office (on a 7-point scale; M = 2,87, SD = 1,25). This score does not deviate much from similar reports of a larger Dutch sample, which uses the same measurement [ 57 ]. Yet, relative to working from home, the office performs better: at home, the burnout tendency is significantly higher compared to the office ( p < .01).

Home versus work: Physical differences.

Fig 2 shows the distribution plots of both the office indoor environmental scores (A-D) and office hardware (E-I) scores. WFH increases the satisfaction with all office indoor environmental factors: Temperature (A), Air Quality (B), Lighting (C), and Noise (D) all score higher as compared to the work environment (mean scores range between 5.37 and 5.13 for the home office, compared to 5.07 and 4.59 for the office; on a 7-point Likert scale). For office hardware, we observe the opposite trend: overall office hardware satisfaction is higher in the office. The satisfaction for the desk (E), chair (F), screen (G), hardware (H), and Wi-Fi (I) range between 5.23 and 4.41 at home, whereas the office hardware satisfaction levels range between 5.52 and 5.37. Table 2 shows that all differences are statistically significant, using the non-parametric Wilcoxon rank sum test and Bonferroni multiple comparison corrections. These observations support the notion that at home, optimizing ergonomics (e.g office hardware factors) remains challenging [ 40 ] while increased individual control over office indoor environment is preferred [ 42 ].

Comparison of satisfaction levels between home and the office environments across various factors. Panels A-D show indoor environment satisfaction ratings for (A) Temperature, (B) Air Quality, (C) Lighting, and (D) Noise, whereas Panels E-I show hardware satisfaction ratings for (E) Desk, (F) Chair, (G) Screen, (H) Hardware, and (I) Wi-Fi. Each plot includes boxplots and data distributions, with orange representing home office and blue representing traditional office settings.

https://doi.org/10.1371/journal.pone.0306475.g002

It is important to confirm that respondents are considering and rating their home office as distinctly different from their office. We correlate each variable’s score at home and at the office. As shown in S2 Table , scores correlate moderately with different variables within the same environment (home office or regular office), but correlations are much lower between the same variables in different environments. For instance, the correlation between temperature and noise at home is r = 0.41, which is considered a moderately strong correlation. Comparatively, the correlation between the temperature at the office and the home office is negligible ( r = 0.06).

Regression results

Explaining productivity and burnout in the home office..

Table 3 shows the estimated standardized coefficients and standard errors of the home office hardware and home office indoor environment variables in explaining productivity. Models 1–4 show that all office hardware variables at home are positively associated with productivity, such that increased satisfaction with each office hardware variable is associated with an increase in productivity when WFH (coefficients ranging from 0.18 to 0.15; SD = .03 to .05). For example, a 1.32 increase of Wi-Fi satisfaction on a 0–7 satisfaction scale translates to a 0.23 increase on a 0–10 productivity scale. This effect is relatively strong, comparable to the effect of, for example, sometimes having children at home during working hours to having no children at home (see S3 Table ). The home office indoor environment variables show a similar pattern: without exception, all variables are associated with increased productivity (coefficients ranging from 0.21 to 0.08; SD = .04). Combining both home office hardware and indoor environment variables in model 3 decreases the size of the coefficients for some variables in the productivity model. Adding additional controls in model 4 hardly affects the model: all office hardware variables remain relevant predictors of productivity, as well as temperature and noise satisfaction (indoor environment).

https://doi.org/10.1371/journal.pone.0306475.t003

Table 3 , models 5–8, show the coefficients for the same home office hardware and home office indoor environment satisfaction on burnout tendency. For the burnout models, the association is negative, meaning that an increase in satisfaction on either variable’s satisfaction is associated with a decrease in the individual level of feeling burnout. The most robust predictors of burnout tendency are desk, chair and Wi-Fi satisfaction (home office hardware), as well as air and noise satisfaction (home office indoor environment).

Comparing both tables shows that, on average, office hardware and indoor environment coefficients and significance levels are generally higher in the productivity models. For example, noise satisfaction is meaningful for both productivity as well as burnout tendency, yet the coefficient is about 50% higher for productivity in all models (0.21 to 0.16 versus -0.13 to -0.09, for productivity and burnout, respectively).

Individual heterogeneity.

Factors other than hardware and indoor environment, for example, household characteristics, may also affect productivity and burnout Tendency. S3 and S4 Tables report the full specifications of Model 3. The results show that the degree to which work can be performed from home does not add predictive value to our model. Women tend to report higher levels of productivity ( δ = 0.15, SD = .07). Not living alone, i.e., having a larger household, decreases burnout score and increases productivity ( δ = -0.10, SD = .04; δ = 0.11, SD = .04, respectively). Having a partner who is not (or only sometimes) home during office hours is associated with increased productivity ( δ = 0.14–0.15, SD = .08) compared to the baseline of having no partner at all. In that sense, having a partner seems good for productivity, if they are not constantly present at home during working hours. For children, a predictable, strong, and linear relationship emerges: burnout tendency increases and productivity decreases when children spend more time at home during working hours. Interestingly, having a dog increases the burnout score significantly ( δ = 0.17, SD = .08).

Finally, previous research indicated that during the pandemic, young employees seemed to appreciate WFH more, and opted for the home office more often as compared to older employees [ 28 ]. Contrasting, we find that the difference between older and younger respondents is negative: the difference between 20-years old versus to 40-years old is an increase in the WFH productivity score of about 0.25 (on a scale from 1–10). In terms of economic significance, this effect is twice as strong as the gender effect on productivity. In addition, we document that older respondents report a stronger willingness to continue to WFH (0.01 standard deviation increase per year of age, SD = .003; see S5 Table ). Together, our results reflect that older workers not only report to be more productive at home and at the office than younger workers, but also seem to have an overall higher willingness to continue to WFH.

Mediation analysis.

We extend our analysis by exploring whether behavior at home (as it relates to using the home office) is associated with satisfaction with hardware and indoor environment. Although office characteristics are fixed or dependent on capital expenditures, the indoor environment can to a large extent be manipulated by human actions. Specifically, we measure the behavior of respondents working from home through active ventilation, both at the extensive and intensive margin.

We implement a mediation analysis through structural equation modelling in order to understand how the home office environment is associated with productivity. Our model specifications show that the ‘Office Hardware’ and ‘Office Indoor Environment’ item loadings are meaningful per latent factor. Further reliability calculations confirm the factor’s consistency, with both factors showing a Cronbach alfa above 0.8 (a = 0.80 and a = 0.85, for ‘Office Hardware’ and ‘Office Indoor Environment’, respectively). Additional model fit tests confirm that our saturated model fits the data well (CFI/TLI > .95, RMSEA close to .05, and SRMR < .05).

First, the latent variables ‘Office Hardware’ and ‘Office Indoor Environment’ have a strong and distinct direct association with WFH productivity, as can be seen in Fig 3 . For both factors, a standard deviation increase is associated with around a 0.3 standard deviation increase in productivity. Second, the percentage of time that the home office is ventilated is significantly associated with both increased hardware and indoor environment satisfaction. Each standard deviation increase in ventilation of the office increases satisfaction with 0.29 and 0.27 points, respectively. Third, ventilation no longer shows a direct association with productivity, which is not captured by its relation to hardware or indoor environment satisfaction (p = 0.88). Hence, the association of ventilation with productivity is fully mediated by satisfaction with hardware or the indoor environment. Both indirect unstandardized parameters via the latent variables are estimated at 0.002, with a total estimated effect of ventilation on productivity of 0.004. Thus, moving from 0% to 100% ventilation of the office is associated with a productivity increase of 0.4 on the 10-point scale through higher hardware or indoor environment satisfaction. Considering that the average productivity score is 6,11 (SD = 1,06), the magnitude of this association is not trivial. This effect equates to 8.18% of the mean and 47% of the standard deviation of the productivity variation in our sample.

Structural equation model depicting the relationships between ventilation, office hardware, office indoor environment, and productivity. Paths are labelled with standardized regression coefficients. Solid lines indicate significant relationships, while the dashed line indicates a non-significant relationship. All coefficients of solid lines are significant at ***p < 0.001.

https://doi.org/10.1371/journal.pone.0306475.g003

Replacing productivity with burnout tendency or willingness to continue WFH in the model shows the same mediation association. Both models, shown in Fig 4 , are well-fitted (both show CFI/TLI > .95, RMSEA close to .05, and SRMR < .05), and for both models, the association runs fully through the latent variables. The total estimated effect of ventilation on burnout tendency is -0.004, with comparable mediation through satisfaction with home office hardware and environment. Moving from 0% to 100% ventilation of the home office is associated with a burnout tendency decrease of 0.4 on the 7-point scale. For the willingness to continue with WFH, the significance and strength of association are stronger for hardware compared as compared to the indoor environment (a = 0.003, p = 0.016; a = 0.005, p < .000, respectively). Moving from 0% to 100% ventilation of the office is associated with an increased willingness to continue WFH of 1.2 on the 10-point scale.

Structural equation model depicting the relationships between ventilation, office hardware, office indoor environment, and willingness to continue with WFH (top) or Burnout propensity (bottom). Paths are labelled with standardized regression coefficients. Solid lines indicate significant relationships, while the dashed line indicates a non-significant relationship. Solid lines coefficients are significant at *p < 0.05, **p < 0.01, and ***p < 0.001.

https://doi.org/10.1371/journal.pone.0306475.g004

Discussion and conclusion

The success of WFH, and the likelihood of its continuation after the pandemic, is dependent on sustained employee satisfaction with and employee productivity in the home office environment. But satisfaction and productivity, in turn, may also be influenced by the physical characteristics of the home office. We use survey data to study the effect of home office satisfaction and environment-improving behavior on productivity, burnout, and willingness to continue with WFH.

Comparing WFH with working from the office first shows that the self-reported productivity is lower at home compared to working at the office. This is contrasting earlier findings based on self-reported productivity, but consistent with multiple non-self-reported outcome analysis [ 6 , 15 , 16 ]. When looking at the physical characteristics of the office, we find that the indoor environmental satisfaction appears higher at home, whereas physical hardware satisfaction such as desks and chairs are preferred at the office. This implies that optimizing ergonomics at home remains challenging [ 40 ] while individually being in control of the indoor environment at home is preferred [ 42 ]. Overall, we find a relatively low score for the willingness to continue WFH, in contradiction to many recent reports, which supports a deeper investigation into factors facilitating successful WFH [ 4 , 58 ].

The association between the both home office hardware as well as indoor environment satisfaction and productivity is profound. Higher satisfaction in both these domains is associated with higher WFH productivity and lower burnout tendency. The majority of all indoor environment and hardware factors included in this paper (with the exception of air quality) are associated with increased productivity and decreased burnout tendency. We find heterogeneity in the reported effects–women and larger households seem to be more productive at home, while having children at home decreases productivity and increases burnout scores. Having a partner increases productivity, but only when they are not around during office hours. Finally, we find that older workers report being more productive, having lower burnout scores, and stating to be more willing to continue to WFH compared to younger workers, contrasting existing evidence [ 28 , 29 ].

To show the influence that real behavior could have on WFH success, we investigate the association of ventilation with productivity. By means of a mediation analysis, we confirm that the amount of time that the home office is ventilated is not only directly associated with increased satisfaction but also indirectly with increased productivity. Practically, we find that changing from not ventilating to ventilating the home office all the time (moving from 0% to 100%) is indirectly associated with 0.5 points on the 10-point scale increased productivity. The magnitude of this estimate on productivity is comparable to moving from no children at home to always having children at home during working hours (0.7-point decrease of productivity). In addition, moving from 0% to 100% ventilating time is associated with 0.4 points on a 7-point scale decreased burnout tendency, and 1.2 points on a 10-point scale increased willingness to continue with WFH. Hence, we find that ventilating the home office is a crucial underlying factor predicting overall satisfaction and is indirectly associated with increased productivity, increased willingness to WFH, and decreased burnout tendency.

Implications

The main contribution of this paper is to show that the physical characteristics of the home office, including the indoor climate, is associated with employee productivity and satisfaction when WFH. Specifically, we not only connect the outcomes of WFH to self-reported satisfaction, but also to behavior that actively influences the indoor environmental quality. The move from the office to the home office needs to be combined with careful design and investment in the quality of the office and its indoor climate. Failure to do so is not only likely to be associated with decreased productivity, but also decreased willingness to work from home, and increased burnout tendency. The physical climate is a determining factor in successful work from home prolongation. As such, this paper reaffirms that the effect of a healthy indoor climate affects productivity, related to previous research that shows significant health effects of indoor climate [ 33 , 34 , 59 , 60 ].

Additionally, our results also suggest that it is crucial to objectively measure the quality of the physical environment, as merely collecting self-reported satisfaction scores might paint an incomplete or even incorrect picture. This is not only shown by the fact that satisfaction scores are influenced by improved ventilation, but also by the fact that self-reported air quality satisfaction, the closest subjective measure related to ventilation, is not associated with productivity. Thus, solely based on self-report analysis, ventilation would have been an unlikely factor considered to improve the success of WFH. Since evaluations of working generally, as well as evaluations of indoor air quality specifically, are heavily reliant on self-reported scores, this conclusion is not trivial.

Limitations

Our results have some limitations. Self-reported data may introduce common method variance, potentially affecting the relationships between predicting, mediating, and outcome variables [ 61 ]. We counter these effects by deemphasizing the compartmentalization of work conditions and characteristics with productivity (outcome) measures. Although we do not measure objective productivity, we at least partially alleviate this concern by using an extensively validated questionnaire.

Second, practical constraints limited our ability to implement a cross-lagged design with multiple measurement points which is considered the normative approach for establishing temporal precedence and causality in mediation analysis [ 62 , 63 ]. Our study employed a cross-sectional design, which has precedent in the literature [ 64 , 65 ], and can still provide valuable insights. We justify our approach based on the model fit and both theoretical as well as literature support of the causal role of air quality improvement on satisfaction and performance [ 36 , 41 , 66 ]. However, we acknowledge that this approach does not allow for the determination of causality with the same rigor as a longitudinal design. Future research should aim to utilize cross-lagged designs to further validate our findings and establish clearer causal relationships.

Third, data on in-office work comes from recall data which may be biased [ 67 ] or influenced by the broader undesirability of pandemic-era work [ 68 , 69 ]. We report on differences between the situation during and before COVID-19 (at the office). To do so, we did not ask our participants at that time, but rather asked them to recollect from memory. Unfortunately, recollection itself is less accurate than asking in the current situation [ 67 , 69 ]. The current situation could even influence the recollected score, as it serves as a reference point [ 51 , 68 ]. The mere fact that WFH is mandatory could put the productivity at work (as well as life in general) in a more generous daylight that it truly was. Taken together, our data quality would have improved if we had foreseen the pandemic, and pretested our subject before the outbreak. Alas.

Finally, the extraordinary circumstances surrounding the COVID-19 pandemic itself could be reflected in our subjective scores, making the observed behaviors, attitudes, and outcomes different from remote work under more typical conditions (mood-as-information theory [ 70 ]. While our research provides valuable insights into the pandemic WFH experience, caution should be exercised when generalizing these findings to other contexts or periods.

In conclusion, we find strong evidence that a favorable home office is associated with multiple WFH success outcomes. Moreover, air-quality-improving behavior is associated with home office satisfaction improvements. The move from the work office to the home office needs to be combined with intentional design and investment in the quality of the office and its climate. Failure to do so will likely have adverse ramifications for the future of WFH [ 5 , 71 , 72 ].

Supporting information

S1 table. correlation table: productivity and stress..

https://doi.org/10.1371/journal.pone.0306475.s001

S2 Table. Correlation table: Hardware and indoor environment.

https://doi.org/10.1371/journal.pone.0306475.s002

S3 Table. Regression results: Full specification (Productivity).

https://doi.org/10.1371/journal.pone.0306475.s003

S4 Table. Regression results: Full specification (Burnout tendency).

https://doi.org/10.1371/journal.pone.0306475.s004

S5 Table. Regression results: Full specification (Willingness to continue WFH).

https://doi.org/10.1371/journal.pone.0306475.s005

S1 Fig. Structural equation model latent variables loading and covariance.

https://doi.org/10.1371/journal.pone.0306475.s006

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Slow productivity worked for Marie Curie — here’s why you should adopt it, too

• Anne Gulland 0

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Marie Curie’s research straddled many decades and involved periods of rest and reflection in the French countryside. Credit: H. Armstrong Roberts/ClassicStock/Getty

Sara, a university professor, describes a typical working day for her as including a barrage of “back-and-forth e-mails, Slack, last-minute Zoom meetings”. These, she says, “prevent me — and everyone in general, I feel — from actually having the time to do deep work, think, write, with high quality”.

Her lot, recounted in Cal Newport’s book Slow Productivity (2024), is one that will be shared by many of her academic peers and other ‘knowledge workers’, the term Newport uses for people whose working day is spent largely thinking about problems and how to resolve them, rather than making a product or directly serving people.

Slow Productivity is a call to arms to reject the performative busyness of the modern workplace, where frequent virtual meetings and long e-mail chains sap so much of workers’ attention. One exhausted postdoctoral researcher interviewed by Newport defined productivity, as it is currently measured in academia, as “working all the time”.

Newport, whose day job is as a computer science at Georgetown University in Washington DC, says that the COVID-19 pandemic and the increase in home working has accelerated a shift towards what he and other workplace commentators term pseudo-productivity or pseudo-activity — rattling through a lengthy to-do list rather than focusing on tasks that require deeper thinking and reflection.

Science must protect thinking time in a world of instant communication

“That, combined with the front-office IT revolution — personal computers, then portable computers with e-mail and networks, and then smartphones — meant that things really began to spin out of control for knowledge workers and the toll of exhaustion and burnout have begun to increase,” he adds.

Instead, Newport urges knowledge workers to “do fewer things. Work at a natural pace. Obsess over quality.” Psychologist Megan Rogers tries to apply this advice, although not always successfully. A fan of Newport’s work, she spent a year tracking her time on a spreadsheet before starting a faculty position at Texas State University in San Marcos. “I try to put no more than five things on my daily to-do list but often get flooded with other, more urgent tasks,” she says. “But I do generally succeed in working at a more natural pace rather than feeling as though I’m rushing. Having flexible working hours helps here. I tend to follow my energy as much as possible, rather than forcing tasks into specific slots of time.”

Learning from the greats

Newport’s book is full of examples of academics and other knowledge workers who have taken radical steps to free themselves from distractions in their quest to produce great work.

Poet and author Maya Angelou, for example, would write in a hotel room with all the artwork removed so that she could focus on her writing. Theoretical physicist Richard Feynman avoided doing committee work and other commitments to focus on the deep thinking his research required. Feynman thought that peace of mind was the most important requisite of creative work, according to his friend and former colleague, computer scientist Stephen Wolfram. In a 2005 talk, Wolfram recalled Feynman’s conviction that “one should always stay away from anything worldly, like management”.

How to manage your time as a researcher

Newport acknowledges that most of us are not like Feynman — people cannot simply ignore their supervisors’ demands while thinking great thoughts. He thinks that academics are prime examples of how work has shifted in recent decades.

“My grandfather was a professor and didn’t own a computer. But his goal was the same as a professor today: to produce books and articles. He produced a ton of books and was very productive. So what have we gained in the twenty-first century, when professors now also send and receive 100 e-mails a day?” he says.

Slow Productivity is Newport’s eighth book. It builds on his previous writing on the dangers of digital distractions, including A World Without Email (2021) and Deep Work: Rules for Success in a Distracted World (2016).

In his weekly podcast , which launched in 2020, he answers listeners’ questions and talks in depth about his ideas on productivity, concentration and distractions. As well as being a book about ideas, Slow Productivity includes practical tips, such as blocking out time in your day when you don’t look at your e-mails (See ‘Seven ways to practise slow productivity in the lab’).

Seven ways to practise slow productivity in the lab

Cal Newport, a computer scientist at Georgetown University in Washington DC, writes books about time management. Here are some of his tips, along with responses by psychologist Megan Rogers, based at Texas State University in San Marcos, who tried three of his suggestions.

Limit daily goals

Newport recommends working on, and obsessing about, one large project a day rather than switching back and forth between multiple big tasks. This is something Newport learnt from computer scientist Nancy Lynch, his doctoral adviser at MIT.

Megan Rogers: Trying to task-batch goals and projects on specific days (or even portions of days) has been extremely helpful to me. I try to schedule student mentorship meetings back-to-back on a couple of days, teaching tasks on a different day and reserve one full day for deep research work (generally focused on one or two papers) without any interruptions. I’m a big advocate of minimizing task-switching as much as possible.

Combine rituals and locations

This will help to develop a regular ‘autopilot’ pattern. Newport cites an academic who, after Friday lunch in the university canteen, chooses the same library booth to work through grant reports before heading back to her office with a coffee.

Introduce docket-clearing team meetings

Have a fixed, weekly meeting to work through pending tasks that require collaboration or clarification, all logged in a shared document. One 30-minute session can save hours of back-and-forth e-mails, says Newport.

Take your time

Notable scientists Nicolaus Copernicus, Galileo Galilei, Isaac Newton and Marie Curie helped to shape the modern scientific enterprise, but the pace at which they worked straddled many decades and involved periods of rest and reflection. Like them, allow your most important work to evolve over a sustainable timeline at various levels of intensity.

Craft a five-year or similar long-term plan for major goals

Newport did so during his doctorate, and it enabled him to pursue a writing career alongside his academic interests, giving him the space to experiment, for example with different writing styles.

M.R.: I have done this loosely for key goals and milestones, but it’s a challenge to get more specific than that. You never know what opportunities might arise and change your plans in the meantime.

Don’t schedule meetings on Mondays

It will help you to ease back into work after the weekend and make Sunday evenings more enjoyable.

M.R.: This is excellent advice but it doesn’t need to be Monday; rather, it could be any day that makes sense for the individual. I sometimes actually like having some of my meetings on Mondays or early in the week to be able to plan and delegate tasks to the team for the week.

Invest in high-quality tools

As a postdoctoral researcher in 2010, Newport bought a US$50 notebook to record lab experiments, thinking that the high-end product made him more structured and careful in his thinking. He recently flipped through it and realized that it contained the seeds of seven peer-reviewed publications.

“A big thing I tried to do in the book is figure out how you can leverage the autonomy you have and how you organize your labour to get away from the worst excesses of super productivity. You can’t say no to a lot of things. But you can have a more transparent workload management system so your boss can see you’re doing all the things you’re being asked to do,” he says.

From student to teacher

It was during his time as a computer science undergraduate at Dartmouth College in Hanover, New Hampshire, that Newport had the idea for his first book, on how to succeed at university.

He got advice from a literary-agent friend and ended up with a US$40,000 advance for How to Win at College (2005).

He then wrote How to be a Straight-A Student (2006), which has sold around a quarter of a million copies, and How to be a High School Superstar (2010).

When he graduated from Dartmouth in 2004, he realized that he wanted a career that would give him the flexibility to continue writing — so he rejected job offers from technology firms and instead began graduate studies at the Massachusetts Institute of Technology (MIT) in Cambridge. There, he gained his master’s and then his doctorate in 2009, for research focused on distributed algorithms — the study of what happens when an algorithm runs on different networks and processors. His adviser at MIT, Nancy Lynch, an engineering and computer scientist, was one of the people who defined the field. They have continued to collaborate.

Computer scientist Cal Newport writes books on work in the digital age. Credit: Penny Gray Photography

At MIT, Newport joined the theory of computation group — a collection of individuals he describes as “preposterous” because of their prodigious achievements.

“It’s the kind of thing a screenwriter would make up,” he says. “One of the other new doctoral students was 16 and had already been out of university for two years working for Microsoft. There was a professor who had just turned 21 and had won a MacArthur Genius Grant at 18 for solving this long-standing theorem,” he says.

“It was super autonomous, super entrepreneurial. We were told to go and find people, have smart ideas and publish,” he adds.

It was among these geniuses that Newport’s ideas on the importance of concentration and focus were seeded.

“I learnt great lessons about the importance of concentration. The students had a distrust of digital technology versus the human brain. These are computer scientists who don’t use computers,” he says, adding, “These ideas infused my thinking. It was an important, formative time, even if I didn’t realize that until later.”

After he graduated he was appointed assistant professor at Georgetown, becoming a research professor in 2023, all the while combining research and teaching with his popular writing.

Fed up and burnt out: ‘quiet quitting’ hits academia

He says that his bosses at Georgetown have been supportive of his non-academic work but adds that his growing success as a writer has not benefited his university career.

“When I was going for tenure, I don’t think I even mentioned that my fifth book had just come out,” he says. “They don’t care about popular-science books.”

In the earlier years of writing and working as an academic, the two parts of his life were separate, but they have now started to converge.

“I’m a computer scientist writing about how technology impacts us and what we can do about that, so I’ve come to realize that it makes sense that my writing and my academic work are connected,” he says.

Newport’s work is now focusing more on technology in society: he is one of the founding faculty members of the Center for Digital Ethics at Georgetown and has introduced a new undergraduate programme in computer science, ethics and society .

He thinks that academics have a duty to their students to demonstrate the importance of focus.

“Academia should be the place where we lean hard into the life of the mind and not be distracted, so we can be exemplars to our students of treating our minds seriously,” he says.

One limitation of Newport’s advice is in its applicability to experimental disciplines, says RNA biologist Maya Gosztyla. Gosztyla is a recent PhD graduate from the University of California, San Diego, who has written about her own time-management tools and techniques , and has read most of Newport’s books, including Slow Productivity . “Newport is a theoretician, so his research schedule is entirely within his control; my stem cells don’t care if it’s a weekend, they still need to be fed,” she says.

Time management for scientists

Although maintaining a regular schedule isn’t always feasible for experimentalists, it’s still something to strive for, she adds. “When I have an unavoidable long day of experiments, or need to maintain cells over the weekend, I try to schedule some lighter days the following week to compensate.”

She continues: “The irregularity of experimental work also emphasizes the importance of maintaining rituals wherever possible, particularly for reading papers and other low-urgency tasks that could otherwise slip through the cracks.”

Gosztyla says that, for her thesis, she specifically sought out a laboratory that would give her the freedom to set her own hours. Also, ruthlessly trimming her to-do list opens up the mental space necessary to think about her science and experiments.

“I believe academia would be a healthier, more productive space if more mentors encouraged this mindset,” she says.

Rogers highlights academia’s obsession with quantity and publication metrics as a potential systemic barrier to implementing Newport’s philosophy, but also argues that quantity and quality are correlated. “The more you do something, the higher quality you tend to do it,” she says. “Obviously, this isn’t necessarily the case for everyone or everything, but I’ve noticed it at least in my own work.”

Nature 632 , 461-463 (2024)

doi: https://doi.org/10.1038/d41586-024-02540-0

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How to Write a Good Topic Sentence? [Steps with Examples]

This is very common in academic writing, sometimes on the identification of the main idea to be focused on by a paragraph. Most students find it hard to write specific and clear topic sentences that sometimes mislead the readers. Being an experienced writer, I have some useful tips for you to write good topic sentences. Just read on, and you can improve your writing capabilities for academic writing with the guidance of WPS AI.

What is a Topic Sentence?

What gives the structure to every paragraph in an academic essay is a topic sentence. It introduces the main idea of the paragraph and thus facilitates the reader's movement through the essay. It is typically located at the beginning of the paragraph and really should specifically state the focus of the paragraph.

A topic sentence can be defined as a short and general summary statement of the main idea in a paragraph. It tells the reader what to expect from the paragraph and keeps the paragraph on track.

Clarity: It should be easy to understand.

Specificity: It focuses on one main idea.

Relevance: It supports the essay's main argument or thesis.

Guidance: It organizes the paragraph and guides the reader.

Focus: It keeps the paragraph on track.

Transition: This links the previous and next paragraphs.

Relationship to the Thesis Statement

Although the thesis statement provides the argument for the whole essay, topic sentences fractionate this argument into sub-points that are discussed in every particular paragraph. This, therefore, helps to ensure that each paragraph supports the overall thesis and a clear structure is maintained in the essay.

How to Write a Topic Sentence in 4 Steps [With Examples]

A topic sentence can help be made much more effective with a clear process for how each paragraph will work together so that it is both organized and effective in your overall essay. Here's a step-by-step guide on how to write a strong topic sentence, with examples and tips for success.

Step 1: Writing an Outline & Identification of the Main Idea

First, make an outline of what the sentence is going to say, and then draft the topic sentence. This helps you not to get sidetracked from your main idea or too wordy about it.

Create an Outline Using WPS AI, you can come up with a comprehensive outline that will give your essay its structure. First, you have to craft a good thesis statement which sums up the purpose and argument of your essay. Next, look for some specific main idea which you will be discussing in each paragraph.

Example Outline:

Thesis Statement: "The rise of remote work changed a lot in terms of productivity, employee satisfaction, and work-life balance."

Paragraph Main Idea: "Through telecommuting, people have been able to increase productivity due to flexible working hours."

WPS AI Function: You can generate visual outlines of what you want to say with WPS AI and keep track of your thoughts. This tool can make sure that all of your ideas are clearly laid out.

Step 2: Write the Topic Sentence

With your outline in place, you can now draft your topic sentence. It needs to be specific, clear, and concise. The language should not be vague, and it must give clear direction to the paragraph.

Topic Sentences: Types

Simple Statement

Definition: A direct statement.

Example: "To this effect, remote work enhances productivity by letting people work where they are most productive."

Definition: A question that introduces what the paragraph is going to talk about.

Example: "How does remote work contribute to improved productivity?"

Definition: It indicates a contrast or difference.

Example: "Unlike traditional office settings, remote work offers unparalleled flexibility that boosts productivity."

Reason and Cause

Definition: It describes the reasons or causes.

Example: "The flexibility in remote work schedules directly leads to higher employee productivity."

Definition: It introduces a list of points.

Example: "Remote work boosts productivity through flexible hours, reduced commuting time, and individually customized workspaces."

WPS AI Function: You can use WPS AI to help you compose and refine topic sentences that are clear and focused on your main ideas.

Step 3: Collect Evidence

After you have composed your topic sentence, brainstorm the evidence, examples, or details you will use to flesh out your main idea. Be sure all supporting details are relevant and directly relate to the topic sentence.

For example, if your topic sentence is that remote work improves productivity, you could use:

Statistics: "According to a 2023 study from Harvard Business Review, remote workers report an increase in productivity of 20%."

Research Studies: "A study by Stanford University showed that remote workers were 13% more productive than their peers working in the office."

Case Studies: "Companies like GitLab and Basecamp have reported significant boosts in productivity as a result of their remote work practices."

WPS AI Function: Using WPS AI, look for relevant data and examples and integrate them to add credibility and depth to the paragraph.

Step 4: Revise & Refine

Finally, refine and revise your topic sentence so that it states what the paragraph contains explicitly and enhances the overall coherence of your essay. Besides that, check clarity and add transitional words if need be to enhance flow.

Example Revision: Original: "Working from home makes me more productive." Revised: "Working from home significantly improves productivity because it provides flexible hours and does not involve any hour-long commutes." WPS AI Function:

Use WPS AI's grammar and style check to perfect your topic sentences so that they are correct and make sense. You can effectively write topic sentences using these steps and the help of WPS AI, which gives your writing clarity and power.

Using WPS to Perfect your Topic Sentence

The easiest and most effective way to write exact and relevant topic sentences is with WPS AI. Here to polish up your writing skills using advanced language capabilities, WPS AI makes sure that clarity and coherence echo through all the lines of your work. Here is how you can use WPS AI to get your topic sentences right:

Checking Grammar and Syntax

WPS AI can assist you in verifying your topic sentences for grammatical, punctuation, and syntactic errors. Clear and error-free language enhances the overall readability of your essays and makes them more credible.

Rephrase Topic Sentence for Clarity

WPS AI will review your topic sentence and suggest rewrites so you can present your intended message more clearly. It can rephrase any awkward language or sections that are ambiguous by creating a revised and more readable version of the sentence.

Automatically Expand/Shorten Topic Sentence

WPS AI can either draw out or shorten your topic sentences so they fit perfectly with what your paragraph requires. This will be very useful, especially in instances where there is an obligation to meet a word count or you would need to compress your topic sentence to a degree.

Sharpen your topic sentences for grammatical correctness, clarity, and details with these advanced features of WPS AI at your beck and call. This way, your write-ups will be more readable, leading to improved quality essays altogether.

What is the difference between a thesis statement and a topic sentence?

The thesis statement tells what the whole paper is about. The topic sentence shows only what the paragraph it is attached to is about.

Where should the topic sentence be placed in a paragraph?

The topic sentence usually is at the very beginning because it immediately introduces the main idea of the paragraph. For stylistic reasons, however, it often occurs in the middle or end.

What should I avoid in a topic sentence?

Paragraphs should not contain vague, over-broad statements or confusing or complicated sentences. Be sure your topic sentence is not just a statement of fact but rather the introduction of an idea to be further developed in the paragraph.

An effective topic sentence is an important part of writing for clarity and conveys an argument to the writer. Just follow these simple steps, and with WPS AI , you shall be guaranteed to generate strong, specific, and engaging topic sentences in a way that maximizes essay quality overall. WPS AI Grammar check, rewriting suggestions, and adjustment of length ensure improvement in your writing efficiency and results.

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Long-term effect of tillage systems on planosol physical properties, co 2 emissions and spring barley productivity.

1. Introduction

2. materials and methods, 2.1. the experimental site, 2.2. experiment design and agricultural practices, 2.2.1. assessment of crop density, 2.2.2. statistical analysis, 2.3. meteorological conditions, 3.1. the effect of tillage intensity on soil physical properties, 3.2. the effect of tillage intensity on soil biological properties, 3.3. the effect of tillage intensity on spring barley productivity indicators, 4. discussion, 5. conclusions, author contributions, data availability statement, conflicts of interest.

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Sinkevičienė, A.; Romaneckas, K.; Jackevičienė, K.; Petrikaitė, T.; Balandaitė, J.; Kimbirauskienė, R. Long-Term Effect of Tillage Systems on Planosol Physical Properties, CO 2 Emissions and Spring Barley Productivity. Land 2024 , 13 , 1289. https://doi.org/10.3390/land13081289

Sinkevičienė A, Romaneckas K, Jackevičienė K, Petrikaitė T, Balandaitė J, Kimbirauskienė R. Long-Term Effect of Tillage Systems on Planosol Physical Properties, CO 2 Emissions and Spring Barley Productivity. Land . 2024; 13(8):1289. https://doi.org/10.3390/land13081289

Sinkevičienė, Aušra, Kęstutis Romaneckas, Karolina Jackevičienė, Toma Petrikaitė, Jovita Balandaitė, and Rasa Kimbirauskienė. 2024. "Long-Term Effect of Tillage Systems on Planosol Physical Properties, CO 2 Emissions and Spring Barley Productivity" Land 13, no. 8: 1289. https://doi.org/10.3390/land13081289

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UiPath: Gen AI Productivity Gains Can Improve Earnings

• UiPath stock down 10% since last coverage, recent SEC filing announced workforce reduction and AI integration, no positive market reaction.
• Market not convinced of benefits of restructuring, concerns about AI focus, need for profitability guidance from management.
• Potential productivity gains from cost-cutting and AI integration could lead to a 25.6% increase in profitability, EPS target of $0.55 by 2026.
• Since more marketing expenses are required to drive sales, cost-optimization through workforce reduction is a sign that things are moving in the right direction.
• The stock could potentially appreciate by 25.6%, but a lot will depend on the profit guidance which is the reason for the Hold position as it is not immune to further volatility.

Since I last covered UiPath ( NYSE: PATH ) in my piece entitled " Execution is Key Amid Tight Macros and Gen AI Disruption" about one month ago, it underwent nearly 8% downside. At that time, I had a hold position, mainly based on the need to see signs of progress on the profitability front.

One such sign emerged in the form of the July 8 SEC filing announcing restructuring actions aimed at reducing the headcount by 10% and applying artificial intelligence to make the platform more innovative. However, there was no positive market reaction, and on the contrary, the stock continued its downtrend and was trading around $11.53 at the time of writing.

My objective with this thesis is to show that the market is probably wrong and that investors need to keep this stock on their radar screens after it lost more than half its value since the beginning of this year. For this purpose, I will show how the combined effect of cost-cutting and AI can lead to enough productivity gains to impact the bottomline by as much as 25% positively.

First, it is important to explain the reasons why the cost-cutting news failed to instill enthusiasm in the stock.

Why the Market Was Not Convinced

An extract of the SEC filing is shown below. As underlined in green, the downsizing aims to reduce costs and improve operational efficiency in a way that reshapes the company's structure. Equally important, the intent is to drive customer centricity which normally means a nimbler salesforce organization.

ir.uipath.com

Now, these sorts of changes take time and the problem is that it costs money too, with the board of directors estimating a sum of $17 million to $25 million as staff-related expenses but also adding that the actual costs may differ materially from what has been estimated. However, no dollar amount was provided concerning the potential benefits, possibly explaining the lack of investor enthusiasm for the restructuring.

Another reason could be the focus on artificial intelligence in research and development.

Now, the company had already embarked on the innovation journey as early as October last year with AutoPilot which uses Gen AI, somewhat similar to Microsoft's ( MSFT ) CoPilot, which is an assistant enabling an employee to accomplish daily office tasks. Therefore, banking on AI feels like Deja vu, especially at a time when investors seem to be concerned about the monetizing aspects, as I detailed in a recent thesis .

Coming to execution, which was one of the reasons for the previous CEO being ousted, the problem was he had not performed well on large deals synonymous with more revenues and profits as tabled below .

Table prepared using data from (seekingalpha.com)

Detailing further, execution is key to ensure that investing time and resources to strengthen the product line is done without unduly increasing operating expenses, especially for a company whose operating expenses constituted 97.5% of its revenues in fiscal 2024 which ended in January. Also, despite showing some progress in controlling costs during the last reported financial year, it remains loss-making as charted below.

Charts prepared using data from (www.seekingalpha.com)

Cost-cutting is Positive and Gen AI Engenders Productivity Gains

In such a context, cost-cutting not only reduces operating expenses, but is also key to ensuring that UiPath can reorient financial resources to "better prioritize go-to-market investments". For this matter, in addition to RPA (robotic process automation) companies, it also faces established ones like the software giant expanding into the RPA market through its Power Automate. Thus, to drive sales, marketing expenses may have to be increased further and cost-cutting is a sign that things are moving in terms of cost optimization.

Furthermore, the restructuring is being championed by founder and new CEO Daniel Dines, someone who is invested in the company and previously held the Chief Innovation Officer role. Thus, with his deep knowledge of the business and its culture, he can better address execution challenges.

Continuing on a positive note, the job cuts have been announced together with investment in AI and making the platform more innovative. This tends to show that this is not just an isolated exercise to streamline operations, but more of a realignment of the workforce with an AI-driven strategy to become customer-centric.

Now, for a company like UiPath, this can take the form of harnessing the power of Gen AI to allow product managers to reallocate resources in a way that more administrative tasks can be handled with the help of an AI assistant. This is not about the work being entirely handled by intelligent algorithms, but rather about using smarter apps to perform daily routine and repetitive aspects of the work while allowing the executive to focus on more creative tasks. As a result, the product manager can have more time to focus on product requirements, press releases, and FAQs (frequently asked questions) related to larger deals, thereby accelerating the time to market. These are what researchers at McKinsey categorize as content-heavy tasks that involve a lot of creative work including brainstorming, synthesizing, writing, and reviewing. In this way, the application of Gen AI to these content-heavy tasks can bring a 40% increase in productivity.

Looking further, there are other activities forming part of the customer management process that are more content-light like data gathering, summarizing them, and building presentations or tasks that are associated with market research. Here, according to McKinsey, productivity could be increased by 15%. Now, assuming that the company has an equal mix of content-heavy and content-light tasks, productivity can potentially be increased by an average of 27.5% as tabled below.

Notably, McKinsey measures productivity by the average completion time of a task, meaning the faster a task is completed, the more productive is an employee. Looking for the relationship between productivity and profitability, according to TGG Accounting , since productivity is the efficiency with which inputs are converted into outputs, achieving more with fewer resources (or man-hours), leads to better profits and competitiveness.

Moreover, to obtain an estimate for the impact of the $17 million to $25 million of additional staff-related expenses on profitability, I divide $25 million (the maximum) by FY-24's revenues of $1,308 million, or 1.9%. Thus, there is a net improvement of 25.6% in profits, as shown below.

Table prepared using data from (www.seekingalpha.com)

Better Productivity means Better EPS

To calculate the impact on the bottomline, I consider the EPS for the fiscal year ending in January 2026 (or lasting from February 2025 to January 2026) as most of this reduction in workforce is expected to occur by the end of the first quarter of the fiscal year 2026 (or from February to April of 2025).

Thus, incrementing by an additional 25.6%, the EPS can potentially climb to $0.55 which conversely decreases the forward P/E ratio from 26.36x to 20.99x (or 0.44/0.55 x 26.36) as shown below.

Table built using data from (seekingalpha.com)

This translates into a target of $14.4 (11.53 x 0.55/0.44) based on applying a 25.6% upside to the current share price of $11.53.

Investors will note that while the EPS of $0.44 for the fiscal year ending in January 2026 does represent a 15.99% increase, it does not seem to factor in the productivity gains because it was revised lower from $0.45 on July 30. This downgrade led to further downside from the beginning of August, as charted below. In this connection, analysts have remained largely pessimistic about the stock since they revised its consensus EPS from $0.68 downward to $0.45 on May 30, or a 34% downgrade, which triggered the major stock downside as shown below.

Comparing the price performances (www.seekingalpha.com)

Therefore, one can expect further downside in case there is a further downgrade till the next quarter earnings call on September 5.

The Way Forward For this Volatile Stock

Noteworthily, the above chart also features Pegasystems ( PEGA ) which is also an RPA play that has outperformed the S&P 500, in sharp contrast to UiPath. Thus, this is a market where there is potential as it is expected to accelerate at a CAGR of 25.7% from 2024 to 2033 but is also one characterized by Gen AI-induced disruption.

To this end, RPA uses software robots (bots) to automate repetitive tasks based on pre-defined strategies , thus freeing up employee time to focus on more value-added tasks, thereby reducing overall costs for businesses and driving up productivity. Now, this is also precisely the aim of Gen AI, which now makes available AI bots that can be used to plan and execute tasks according to the aims of the user. However, one big difference is that in addition to carrying out simple repetitive tasks, supersmart apps driven by LLMs (large language models) can also perform more complex work requiring a larger degree of autonomy, for example learning from mistakes.

Thus, unless UiPath's management convinces investors that the workforce reduction plan accomplished in conjunction with the use of AI to drive innovation across the platform is aligned with the go-to-market strategy and is done profitably, the stock may suffer further. At the same time, they should explain how they will leverage the recently developed DocPATH and CommPATH, two customized LLMs to make the task of processing any document type including different formats faster.

Thus, a lot will depend on execution and the profit guidance and this is the reason why despite having shown that there is potential for a 25.6% upside, I still have a hold position. Finally, for those who are holding on to UiPath's shares, it was equipped with $1.94 billion of cash versus $76.6 million of debt at the end of April meaning ample financial firepower to carry out an in-depth organizational restructuration while applying a higher dose of AI to improve productivity.

This article was written by

Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. This is an investment thesis and is intended for informational purposes. Investors are kindly requested to do additional research before investing.

Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.

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