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• Published: 12 June 2024

Hybrid working from home improves retention without damaging performance

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

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

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

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

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

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

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

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

The experiment

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

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

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

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

Effects on employee retention

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

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

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

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

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

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

Employee performance and promotions

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

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

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

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

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

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

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

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

Self-assessed productivity

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

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

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

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

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

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

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

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

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

Location and set-up

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

Randomization

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

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

Employee characteristics and balancing tests

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

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

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

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

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

Null results

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

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

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

Volunteer versus non-volunteer groups

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

Performance subcategories

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

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

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

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

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

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

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

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

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

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

Data sources

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

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

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

Inclusion and ethics statement

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

Reporting summary

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

Data availability

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

Code availability

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

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Acknowledgements

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

Author information

These authors contributed equally: Nicholas Bloom, Ruobing Han

Authors and Affiliations

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

Nicholas Bloom

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

Ruobing Han

National School of Development, Peking University, Beijing, China

James Liang

Trip.com, Shanghai, China

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Contributions

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

Corresponding authors

Correspondence to Nicholas Bloom , Ruobing Han or James Liang .

Ethics declarations

Competing interests.

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

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Extended data figures and tables

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

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

Extended Data Fig. 2 Home (October 2021).

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

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

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

Extended Data Fig. 4 Trip.com revenues.

Trip.com revenues from 2000 to 2023.

Supplementary information

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

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Reflecting on Work Improves Job Performance

Many of us are familiar with the gentle punishment known as "time-out," in which misbehaving children must sit quietly for a few minutes, calm down, and reflect on their actions.

New research suggests that grown-ups ought to take routine time-outs of their own, not as a punishment, but in order to improve their job performance.

“Our work shows that if we'd take some time out for reflection, we might be better off.”

In the working paper Learning by Thinking: How Reflection Aids Performance, the authors show how reflecting on what we've done teaches us to do it more effectively the next time around.

"Now more than ever we seem to be living lives where we're busy and overworked, and our research shows that if we'd take some time out for reflection, we might be better off," says Harvard Business School Professor Francesca Gino , who cowrote the paper with Gary Pisano , the Harry E. Figgie Professor of Business Administration at HBS; Giada Di Stefano, an assistant professor at HEC Paris; and Bradley Staats, an associate professor at the University of North Carolina's Kenan-Flagler Business School.

The research team conducted a series of three studies based on the dual-process theory of thought, which maintains that people think and learn using two distinct types of processes. Type 1 processes are heuristic—automatically learning by doing, such that the more people do something, the better they know how to do it. Type 2 processes, on the other hand, are consciously reflective, and are often associated with decision making.

Essentially, the researchers hypothesized that learning by doing would be more effective if deliberately coupled with learning by thinking. They also hypothesized that sharing information with others would improve the learning process.

Reflection, sharing, and self-efficacy

For the first study, the team recruited 202 adults for an online experiment in which they completed a series of brain teasers based on a " sum to ten " game. A round of problem solving included five puzzles, and participants earned a dollar for each puzzle they solved in 20 seconds or less.

After recording the results of the first problem-solving round, the researchers divided participants randomly into one of three conditions: control, reflection, and sharing.

In the control condition, participants simply completed another round of brain teasers.

In the reflection condition, participants took a few minutes to reflect on their first round of brain teasers, writing detailed notes about particular strategies they employed. Then they, too, completed a second round of puzzles.

In the sharing condition, participants received the same instructions as those in the reflection group, but with an additional message informing them that their notes would be shared with future participants.

Results showed that the reflection and sharing group performed an average of 18 percent better on the second round of brain teasers than the control group. However, there was no significant performance difference between the reflection and the sharing group. "In this case sharing on top of reflection doesn't seem to have a beneficial effect," Gino says. "But my sense was that if the sharing involved participants actually talking to each other, an effect might exist."

Next, the researchers recruited 178 university students to participate in the same experiment as the first study, but with two key differences: One, they were not paid based on their performance; rather, they all received a flat fee. Two, before starting the second round of brain teasers, they were asked to indicate the extent to which they felt "capable, competent, able to make good judgments, and able to solve difficult problems if they tried hard enough."

As in the first study, those in the sharing and reflection conditions performed better than those in the control group. Those who had reflected on their problem solving reportedly felt more competent and effective than those in the control group.

"When we stop, reflect, and think about learning, we feel a greater sense of self-efficacy," Gino says. "We're more motivated and we perform better afterward."

A field experiment

The final study tested the hypotheses in the real-world setting of Wipro, a business-process outsourcing company based in Bangalore, India. The experiment was conducted at a tech support call center.

The researchers studied several groups of employees in their initial weeks of training for a particular customer account. As with the previous experiments, each group was assigned to one of three conditions: control, reflection, and sharing. Each group went through the same technical training, with a couple of key differences.

In the reflection group, on the sixth through the 16th days of training, workers spent the last 15 minutes of each day writing and reflecting on the lessons they had learned that day. Participants in the sharing group did the same, but spent an additional five minutes explaining their notes to a fellow trainee. Those in the control condition just kept working at the end of the day, but did not receive additional training.

Over the course of one month, workers in both the reflection and sharing condition performed significantly better than those in the control group. On average, the reflection group increased its performance on the final training test by 22.8 percent than did the control group. The sharing group performed 25 percent better on the test than the control group, about the same increase as the reflection group.

This was in spite of the fact that the control group had been working 15 minutes longer per day than the other groups, who had spent that time reflecting and sharing instead.

Gino hopes that the research will provide food for thought to overworked managers and employees alike.

"I don't see a lot of organizations that actually encourage employees to reflect—or give them time to do it," Gino says. "When we fall behind even though we're working hard, our response is often just to work harder. But in terms of working smarter, our research suggests that we should take time for reflection."

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Job performance in healthcare: a systematic review

Marcel krijgsheld, lars g tummers, floortje e scheepers.

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Received 2020 Sep 9; Accepted 2021 Nov 30; Collection date 2022.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Healthcare organisations face major challenges to keep healthcare accessible and affordable. This requires them to transform and improve their performance. To do so, organisations must influence employee job performance. Therefore, it is necessary to know what the key dimensions of job performance in healthcare are and how these dimensions can be improved. This study has three aims. The first aim is to determine what key dimensions of job performance are discussed in the healthcare literature. The second aim is to determine to which professionals and healthcare organisations these dimensions of job performance pertain. The third aim is to identify factors that organisations can use to affect the dimensions of job performance in healthcare.

A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The authors searched Scopus, Web of Science, PubMed, and Google Books, which resulted in the identification of 763 records. After screening 92 articles were included.

The dimensions – task, contextual, and adaptative performance and counterproductive work behaviour – are reflected in the literature on job performance in healthcare. Adaptive performance and counterproductive work behaviour appear to be under-researched. The studies were conducted in different healthcare organisations and pertain to a variety of healthcare professionals. Organisations can affect job performance on the macro-, meso-, and micro-level to achieve transformation and improvement.

Based on more than 90 studies published in over 70 journals, the authors conclude that job performance in healthcare can be conceptualised into four dimensions: task, contextual and adaptive performance, and counterproductive work behaviour. Generally, these dimensions correspond with the dimensions discussed in the job performance literature. This implies that these dimensions can be used for further research into job performance in healthcare. Many healthcare studies on job performance focus on two dimensions: task and contextual performance. However, adaptive performance, which is of great importance in constantly changing environments, is under-researched and should be examined further in future research. This also applies to counterproductive work behaviour. To improve job performance, interventions are required on the macro-, meso-, and micro-levels, which relate to governance, leadership, and individual skills and characteristics.

Keywords: Systematic review, Job performance, Task performance, Contextual performance, Adaptive performance, Counterproductive work behaviour, Healthcare

Together with governments and policymakers, healthcare organisations face major challenges to ensure healthcare remains accessible and affordable. This requires healthcare organisations to transform and improve their performance. These challenges cannot be met without the involvement and excellent performance of healthcare employees.

The Organisation for Economic Cooperation and Development (OECD) expects that in 2050, almost 27% of the population will be over 65 years old and more than 10% will be over 80 [ 1 ]. This may lead to increasing demand for healthcare. According to the OECD, healthcare expenditure in terms of gross domestic product will grow from 8.8% in 2017 to 10.2% in 2030 in OECD countries [ 1 ]. A record amount of money is being spent on healthcare, and this is expected to further increase due to pressure arising from, among other factors, an ageing population. However, advances in medical technology and rising public expectations regarding healthcare services also contribute to increasing health expenditure [ 2 , 3 ]. Accessibility is not the only challenge arising from an ageing population and the consequent increasing demand for care; a shortage of healthcare professionals is another major challenge healthcare organisations face [ 4 , 5 ]. All these challenges make healthcare perhaps one of the most important areas in which the change and improvement of organisational performance are necessary [ 2 ]. As healthcare is mainly people work, change and improvement in organisational performance will be closely linked to the performance (i.e., the actions and behaviours) of employees [ 6 ]. In other words, the job performance of healthcare professionals is of crucial importance to achieve organisational goals [ 6 – 8 ].

Job performance has been widely discussed and conceptualised in various ways [ 8 ]. This is reflected in Koopmans et al.’s [ 9 ] systematic review, in which the authors identify 17 generic and 18 job-specific frameworks. The job-specific frameworks in that study relate to the army and employees and management in the service and sales sector. However, Greenslade and Jimmieson’s (2007) framework was developed for the healthcare sector [ 10 ] based on Borman and Motowidlo’s theoretical model [ 11 ]. Based on the 35 frameworks Koopmans et al. identify four main dimensions: task performance, contextual performance, adaptive performance, and counterproductive work behaviour [ 9 ].

Task performance has a direct relationship with the organisational technical core [ 11 – 14 ]. The term refers to direct activities (such as treating patients) and indirect activities (such as hiring nurses) that are a formal part of a worker’s job [ 15 ]. Task performance is seen as an encompassing dimension that also includes aspects such as task behaviour [ 16 ], job and non-job specific tasks [ 17 ], role performance [ 18 ], technical activities [ 19 ], and action orientation [ 20 ]. Contextual performance includes, among other items, interpersonal behaviour [ 16 ], organisational citizenship behaviour [ 21 ], extra role performance [ 22 ], and peer team interaction [ 23 ]. Contextual performance concerns the broader organisational, social, and psychological environment in which a technical core must function [ 11 – 14 ]; it includes activities such as volunteering for extra work and maintaining good interpersonal relationships [ 15 ]. Adaptive performance refers to the extent to which an individual adapts to changes in work systems or work roles [ 9 ]. It is also defined as adaptability and pro-activity [ 24 ] and creative performance [ 21 ]. Attention towards adaptive performance has increased in recent decades due to the dynamic nature of work environments [ 25 ]. In earlier frameworks, adaptive performance was seen as a separate dimension [ 26 – 28 ] instead of a component of contextual performance [ 29 ]. Finally, counterproductive work behaviour refers to behaviour that is harmful to the performance of an organisation [ 30 ]. It includes, for instance, off-task behaviour, unruliness, theft, drug abuse [ 29 ], absenteeism (not attending work) and presenteeism (attending work while ill [ 31 – 33 ];).

To change and improve the performance of healthcare professionals, and thus the performance of healthcare organisations, it is important to determine whether the four dimensions can be used as a reference for job performance research in healthcare. Although Greenslade and Jimmieson (2007) propose a framework, it focuses specifically on nurses and only includes the task and contextual performance dimensions, thus having little applicability in healthcare research in general. Therefore, it is important to determine how job performance in healthcare is treated in the research literature and whether it relates to the dimensions of task, contextual, and adaptive performance and counterproductive work behaviour. To arrive at findings about whether the four dimensions can be applied to the broad field of healthcare, it is important to investigate in which sectors of healthcare and in relation to which professionals the dimensions have been used in research. Finally, to change and improve the performance of the healthcare professional, it is relevant to determine how and at which level organisations can implement changes to affect job performance. In summary, the purpose of this review is to answer the following questions:

Which of the four job performance dimensions are described in studies focusing on job performance in healthcare?

To which professionals and health organisations do the dimensions of job performance discussed in the studies pertain?

How and on which level can organisations affect the job performance of healthcare professionals?

This research was accomplished by conducting a systematic literature review. The method section describes the process of identification, screening, and assessing the eligibility of studies. The results section begins with an overview that sets out the distribution of the studies. The overview reveals in which year, and in which journal the articles were published. It also details whether studies were carried out in developed or developing countries. Further, this paper explains how it assesses the methodological quality of the studies. Following this overview, this paper presents the answers to the research questions, beginning first with the job dimensions identified in the selected studies, and then proceeding to an analysis of the type of organisations the studies examined and the healthcare professionals to which the studies pertain. Finally, the results section describes the factors that can affect job performance at different organisational levels. The discussion section discusses the results and reflects on a few of this paper’s limitations. The conclusion section provides suggestions that can be used for future research on job performance in healthcare based on this study’s findings.

The literature search was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 34 ]. To find eligible studies, four databases were searched: Scopus, Web of Science, PubMed, and Google Books. The goal of the research strategy was to find articles and books that relate to job performance in healthcare and include a broad scope of healthcare professionals. The search strategy is detailed in Appendix A .

Eligibility criteria

Studies included in the review must meet the following criteria. They must relate to job performance in the field of healthcare. Job performance or comparable terms, such as work performance or work behaviour, must appear in the title or abstract. Studies that examine at least one of the four dimensions or related terms are also eligible. Studies published between 1996 and December 2019 were selected. As part of the pragmatic approach to gathering literature, only studies written in English were considered. All articles published in international journals that were selected for this study must have been peer-reviewed.

Study selection

Through the search strategy, 763 records were identified, including four books. After 17 duplicates were removed, the titles and abstracts of the remaining 747 records were screened. This resulted in the exclusion of 497 records (including three books). Although the studies are related to healthcare, job performance was not the main objective of these studies. For example, a few studies examine musculoskeletal disorders and their impact on nursing tasks [ 16 , 17 ]. Other studies focus on job satisfaction [ 18 , 19 ]. After the exclusion of these 497 studies, the authors read the remaining 250 articles in detail and analysed their eligibility. This resulted in the exclusion of another 158 studies. The grounds for exclusion are as follows. Studies that focus on a specific task, such as working with electronic healthcare systems [ 20 , 21 ], radiation therapy [ 35 ], cervical screening [ 36 ], and communication in the operating theatre [ 24 , 25 ], were excluded.

Full-text articles were not available for two studies. After completing the process of screening and analysing the articles, a total of 92 articles, including one book chapter, met the eligibility criteria. The study selection process is depicted schematically in Fig.  1 using the PRISMA flowchart [ 34 ].

Flowchart study selection

After categorising the articles by year of publication and the journals and countries in which they were published, the methodological quality of the studies was assessed using the integrated quality criteria for the review of multiple study designs [ 37 ]. Studies that could not be assessed using the ICROMS tool were assessed using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers [ 38 ]. Because not all the selected studies directly refer to task, contextual, or adaptive performance or counterproductive work behaviour, it was imperative to assign terms, such as nursing work, tasks, or activities and indirect or direct care [ 27 , 28 ] to one of the dimensions. The assignment of the terms was accomplished using the definitions of the four dimensions. To determine whether the dimensions of job performance were used in the broad field of health care, the type of organisation in which job performance was studied was examined. In addition, it was analysed to which professionals these studies related. Finally, the factors influencing job performance were categorised into macro-, meso-, and micro-level factors. All coding can be viewed on the Open Science Framework (OSF) database.

Before answering the research questions, this paper provides an overview that sets out the distribution of the studies. The overview reveals in which year and in which journal the articles were published. It also shows whether the studies were carried out in developed or developing countries. Results of the assessment of the methodological quality of the studies are provided below.

Distribution of the studies

Table  1 reveals that most studies (82.6%) were conducted in developed countries (e.g., [ 39 – 41 ]), with the United States being the most common study location (29.4% of all studies; e.g., [ 42 – 44 ]). With regard to developing countries, China was the most common study location (e.g., [ 45 , 46 ]).

Distribution of the articles in developed and developing countries

a Based on the IMF World Economic Outlook Database, October 2018: https://www.imf.org/en/Publications/WEO/weo-database/2018/October

b See Appendix B . c Studies conducted in two countries

The articles included in this review were published in 76 different journals ( Appendix C ). The journals can be divided into healthcare fields, such as nursing [ 47 ], medicine [ 42 ], healthcare [ 48 ], and psychology [ 49 ], and into journals with a focus on specific topics, such as maternity [ 50 ] , ergonomics [ 51 ], and critical care [ 52 ]. Almost 20% of the articles were published in the following four journals: BMC Health Services Research, the Journal of Advanced Nursing , the International Journal of Medical Informatics, and the Journal of Managerial Psychology . Most of the studies were conducted in a single country, which raises questions about their external validity.

Figure  2 illustrates the publication years of the studies, divided into publications in developed and developing countries. It indicates that job performance in healthcare has been studied almost continuously over the years and is still of interest. Figure 2 also suggests that the interest in job performance in healthcare has increased in developing countries over the last decade.

Number of publications on job performance in healthcare, 1996–2019

Design and quality of the studies

To assess the methodological quality of the studies, the ICROMS quality assessment tool was used [ 37 ]. The tool provides a comprehensive set of general and specific quality criteria for randomised controlled trials (RCTs), controlled before-after (CBA) studies, non-controlled interrupted time series (NCITS) studies, cluster-randomised controlled trials (cRCTs), and non-controlled before-after (NCBA) studies. The ICROMS tool also provides a clear and transparent scoring system with a minimum required score per study design. The results of the study designs are listed in Table  2 . The ICROMS scores of the assessed studies are shown in the OSF database. Qualitative and cohort studies, CBA studies, RCTs, and NCITS studies all achieved the minimum required score. Although the minimum required score was achieved in these studies, room for improvement exists. About 60% of the studies suffer from selective outcome reporting due to unavailable study protocols. Clear statements as to whether or not the studies were selectively reported did not solve the issue with the lack of protocols. On average, only the NCBA studies failed to meet the minimum required score because no baseline measurements were conducted, and no attempt was made to mitigate the effect of not having a control group. Although the quality of these NCBA studies is low, one can nonetheless provide some commentary on them. For instance, not all ICROMS items could be evaluated because it is unclear whether the criteria were met. The lack of evidence that this cannot be ascertained from an article does not mean that the criteria have not been applied. Researchers can accomplish improvement by providing a better description of the method of subject selection and its characteristics.

Results of the assessment of the methodological quality of the studies, assessed using ICROMS

The ICROMS tool has a scope for further development of quality criteria applicable to additional study designs, such as surveys and cross-sectional studies [ 37 ]. Therefore, studies that rely solely on data from questionnaires could not be assessed using the ICROMS tool. These studies (e.g., [ 30 , 53 ]) were assessed using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers [ 38 ]. The overall score ranged from 0.72–1.0 (mean: 0.91, standard deviation: 0.07).

Dimensions of job performance

The first research question examines which of the four dimensions of work performance (i.e., task, context, and adaptive performance and counterproductive work behaviour) are described in studies of work performance in healthcare. The results show that these dimensions are applicable to work performance in healthcare.

The review of the literature revealed studies that directly refer to Motowidlo et al. [ 11 ], who classify and define job performance as task and contextual performance (e.g., [ 46 , 49 , 54 ]). Studies were also found that directly refer to Greenslade and Jamieson [ 10 ], who suggest a model based on Motowidlo and Van Scotter’s [ 55 ] classification of methods to measure the job performance of nurses, which is directly linked to two dimensions, task and contextual performance (e.g., [ 56 – 58 ]). Studies referring to organisational citizen behaviour (e.g., [ 59 , 60 ]) were classified as contextual performance because there is significant overlap between the definitions of organisational citizen behaviour and contextual performance [ 9 ]. Overlap was also found in studies that directly refer to counterproductive work behaviour (e.g., [ 61 , 62 ]). In addition to the studies that directly refer to the dimensions of job performance, other studies described task, skill, and behavioural performance without a direct reference to the dimensions of job performance. The definitions [ 9 ] listed in Table  3 were used by the researchers to assign these tasks, skills, and behaviours to one of the dimensions of job performance if they were in alignment with those definitions.

Definitions of the four dimensions of job performance based on Koopmans et al.’s review (2011)

Patient feeding [ 63 ], direct patient contact [ 64 ], scheduling toileting [ 65 ], and speaking with other professionals concerning patient care [ 66 ] are examples of tasks that were attributed to the task performance dimension because these examples are part of a healthcare professional’s job. Visiting unit and hospital meetings [ 67 ], continuing professional development [ 68 ], and tutoring trainees [ 69 ] were attributed to contextual performance because these examples contribute to the improvement of an organisation overall. The willingness to implement organisational changes [ 70 ] and the eagerness to require professional information [ 71 ] are examples of behaviours that were attributed to adaptive performance because they are important to adapt to changes in work systems and roles. Purposely failing to help a colleague [ 72 ] and rude behaviour among supervisors [ 73 ] are examples of behaviours that were attributed to the dimension of counterproductive work behaviour because these behaviours can lead to employee illness and increase turnover and therefore harm an organisation’s well-being. A full description of the allocation of the studies within this paper’s sample to the dimensions is available on the OSF database. All tasks, skills, and behaviours can be assigned to one of the four dimensions of job performance. Along with the studies that directly refer to these dimensions, Table  4 lists the assignment results.

The distribution (or combinations) of dimensions of job performance

a See Appendix B . b References in bold italics concern studies in which task and contextual performance both occur. c Underlined references concern studies that bring together task, contextual, and adaptive performance. d References in italics refer to studies about task and contextual performance and counterproductive work behaviour. e Underlined and italicised references refer to studies with combined dimensions

The results reveal that over 47% of the studies focus on task performance, such as primary care tasks [ 36 ], supportive care [ 50 ], and manual tasks [ 74 ]. They also show a focus on contextual performance, which is about team interdependence, communication, synchronicity, coordination and confidence in interprofessional collaboration, and knowledge sharing [ 75 ]. A total of 45 studies investigates contextual performance in combination with task performance. This follows logically from Motowidlo et al.’s [ 11 ] frequently used definition of job performance. Thirteen studies focus on counterproductive work behaviour, which includes abuse, production deviance, sabotage, theft, absence, early and late arrival [ 61 ], workplace violence, verbal aggression, harassment, intimidation, threats, and bullying [ 76 ]. Only eight studies include the adaptive performance dimension; for example, some studies examine adopting electronic health record systems [ 77 ], adopting new innovations [ 71 ], creativity, or personal initiatives [ 59 ].

Healthcare organisations and professionals

The second research question concerns the type of healthcare organisations in which the studies investigate job performance and the type of healthcare professionals to which the studies pertain. The studies examine job performance in several healthcare fields and with respect to various types of healthcare professionals. Table  5 lists the types of healthcare organisations the studies examine. It indicates that over 77% of the studies were performed in hospitals (e.g., [ 78 , 79 ]), including in cardiology, general surgery, anaesthetics [ 80 ], and psychiatry [ 39 ] wards or in special hospitals such as children’s hospitals [ 45 , 81 ]. Other studies investigate job performance in hospices [ 82 ], organisations for patients with special needs [ 59 ], and nursing homes [ 36 ]. In six studies, the research was performed in both hospitals and other healthcare organisations. One study did not specify the type of healthcare organisation the authors studied [ 83 ].

Healthcare organisations where research into job performance was conducted

a See Appendix B . b References in bold italics indicate studies conducted in both hospitals and other health care organisations. c Includes homes for special needs patients and hospices, outpatient care, pharmacies, and community centres

About 52% of studies in the sample concern the job performance of nurses (e.g., [ 53 , 84 ]; see Table  6 ). Besides general nurses, several studies also focus on intensive care nurses [ 52 , 85 ] and maternity nurses [ 50 ]. In about 26% of the studies, physicians (e.g., [ 42 , 86 ]), such as paediatricians [ 81 ] and gynaecologists [ 77 ], are the focus of attention. Eighteen studies investigate the job performance of other healthcare professionals, such as pharmacists [ 87 , 88 ], lab technicians [ 61 ], and administrative employees [ 72 ]. Five studies do not specify the type of professional the authors examined. Markon, Chiocchio, and Fleury discuss healthcare professionals in general [ 75 ].

Investigated healthcare professionals in each study

a See Appendix B . b References in bold italics concern studies on both nurses and physicians. c References in italics that are underlined concern studies on nurses and other healthcare professionals. d Includes personal care workers, mental healthcare professionals, pharmacy staff, caregivers, administrative employees, final-year medical students, care assistants, administrative staff, counsellors, psychologists, pharmacists, social workers, lab technicians, and supervisors

Factors affecting the job performance of healthcare professionals

To answer the third research question, which concerns factors that affect the healthcare professionals’ job performance, this study distinguishes between the macro-level (organisation), meso-level (management/team), and micro-level (individual). This distinction reveals that the job performance of healthcare professionals can be affected on all three levels.

On the macro-level, job performance can be affected by how an organisation is structured [ 82 ], the extent to which a healthcare professional perceives that they have organisational support [ 53 , 73 ], and organisational culture [ 89 ]. Employee performance can flourish in an innovative atmosphere [ 71 ]. In contrast, job performance is likely to decrease in a toxic organisational climate and in cases where supervisors act abusively [ 61 , 90 ]. Turnover of high-performing employees can also affect an organisation’s performance negatively [ 54 ].

At the meso-level, managerial support and supervision and training programmes contribute to job performance levels [ 75 , 76 , 91 ]. In addition, factors such as interdependence [ 75 ], team structure [ 88 ], and the presence of social support [ 57 , 92 ] can affect job performance. Positive views towards work and innovation in organisations with employee-centred designs [ 93 ] contribute positively to job performance. Factors that negatively affect job performance on the meso-level include abusive supervision [ 94 ], limited resources, heavy workloads and dissatisfaction with co-workers [ 76 ], and burnout [ 95 ].

On the micro-level, the extent of work engagement, role clarity, and autonomy [ 53 , 96 ], as well as employee skills and education levels [ 58 ], overwork [ 69 ], and the prevalence of multitasking [ 64 ] are relevant factors that influence job performance. Other relevant factors that influence job performance applies to employees’ personal characteristics, such as openness to change and extraversion [ 56 , 67 , 97 ], seeking challenges [ 70 ], eagerness [ 71 ], and creativity [ 59 ]. Low emotional intelligence [ 98 ] and Machiavellianism – pragmatic, emotionally detached, and task oriented as.

opposed to person oriented – affect job performance in a negative manner [ 45 ]. In summary, the governance of an organisation, the style of management or leadership, and the individual skills and characteristics of the professionals at an organisation can improve or diminish the performance of individual employees. This, in turn, can affect organisational performance (Table  7 ).

Factors affecting job performance on the macro-, meso-, and micro-levels

To the best of the authors’ knowledge, this paper appears to be the first systematic review of the dimensions of job performance in healthcare, given that the study selection research process only produced one study that examine frameworks on job performance in healthcare. This one exception concerns Greenslade and Jimmieson’s framework; however, their study focuses specifically on nurses and thus is not broadly applicable to the field of healthcare [ 10 ]. The review in the instant paper also provides an important contribution by gathering knowledge on job performance in healthcare through an examination of articles published in 76 different journals. Most of these studies were conducted in single countries and often within the same types of healthcare organisations, which limits their generalisability. The interest in job performance in developing countries has only become apparent over the last decade. The methodological quality of the sample studies was assessed, revealing that most studies met the minimum required score. Although this minimum score was required, there is room for improvement in the literature, as over 60% of the studies suffer from selective outcome reporting due to the unavailability of study protocols. Along with improving generalisability, these issues should be considered in future research on this topic.

Studies concerning job performance in healthcare tend to apply at least one of the four dimensions of job performance. Studies without a direct reference to the task, contextual, or adaptive performance or counterproductive work behaviour dimensions offer descriptions of the activities, skills, and behaviours of healthcare employees. Based on the definitions of the dimensions, these activities, skills, and behaviours are attributable to at least one of the dimensions of job performance. Therefore, future studies about job performance in healthcare could be built on these dimensions.

Although the four dimensions do appear in healthcare literature concerning job performance, there is a discrepancy in the extent to which the dimensions have been studied. Task performance (49%) and contextual performance (39%) have been exhaustively investigated, whereas adaptive performance (8%) – which is also of great importance in constantly changing environments such as healthcare – appears to be under-researched. The same is true of the counterproductive work behaviour dimension, which can have a substantial and negative effect on job performance. Authors should consider this gap in job performance research in future research endeavours.

This review shows that scholars have studied the dimensions in different types of healthcare organisations and with reference to a variety of healthcare professionals. The main type of healthcare organisation the studies examine is hospitals and the departments and wards within them. About 22% of the studies were conducted in nursing homes, community centres, and home care organisations (among other organisations). Because most studies were conducted in hospitals, it was expected that most of the surveyed professionals would be physicians (26%) and nurses (52%). Other professionals the studies examine include mental healthcare professionals, psychologists, pharmacists, lab technicians, and supervisors. Consequently, the results show that the task, contextual, and adaptive performance and counterproductive work behaviour dimensions all apply to the broad field of healthcare and pertain to professions that exist within the healthcare sector. As such, these dimensions are useful for examining job performance in the broad context of healthcare and healthcare professionals.

This research not only investigated which dimensions of job performance can be used in the context of healthcare but also how and at what level these dimensions could be affected. The results show that the job performance of healthcare professionals can be affected on three levels. On the macro-level, the structure of an organisation, support for the board among an organisation’s employees, and organisational culture are examples of factors that affect job performance. At the meso-level, job performance can be affected to how management acts, how work is organised, and how teams function. On the micro-level, job performance is affected by employee motivation, the educational levels of the professionals in question, and employees’ personal characteristics. These levels are interdependent. Thus, organisations cannot simply improve the job performance of healthcare professionals in isolation from other efforts, and research aimed at improving job performance must be conducted with reference to these three levels. Given the apparently limited research regarding the adaptive performance and counterproductive work behaviour dimensions in healthcare, this paper suggests researchers investigate these dimensions with reference to the factors at the aforementioned levels to influence these dimensions.

Limitations

The review set out in this paper has a few limitations. First, it is not certain that the review identified and covered all studies concerning job performance in healthcare. One reason for this is the fact that only English articles were eligible for inclusion based on the eligibility criteria. By including studies that were conducted in non-English speaking regions and in both developed and developing countries, this paper tries to reduce the impact of this potential limitation. Second, since the search criteria focused on at least one of the four dimensions, there is a possibility that other potential dimensions may not have emerged from the results. A possible third limitation is based on the fact that job performance is described in many ways, and there are many different terms that could be related to dimensions of job performance. Finally, the ratio between studies that were conducted in developed and developing countries within the sample implies a validation risk. However, studies that were conducted in either developed or developing countries are referred to in Greenslade and Jimmieson’s [ 10 ] and Motowidlo et al. [ 11 ] works. Despite these limitations, the findings in this review provide support for further research on job performance in healthcare.

This research aimed to provide a concept that can be used for research on job performance in healthcare. Based on an examination of more than 90 studies published in over 70 journals, this research shows that job performance in healthcare can be conceptualised into four dimensions: task, contextual, and adaptive performance, and counterproductive work behaviour. While some of the studies directly refer to these dimensions, other studies describe tasks, skills, and behaviours without making direct reference to the four dimensions. However, these tasks, skills, and behaviours were assigned to one of the dimensions of job performance if they were in alignment with their definitions. In healthcare studies on job performance, the focus is on task and contextual performance. However, adaptive performance, which is of great importance in a constantly changing environment, is under-researched and should be considered a topic for future research. This is also suggested for the counterproductive work behaviour dimension. To improve job performance, interventions – in conjunction with one another – are required on the macro-, meso-, and micro-levels, which concern governance, leadership, and individual skills and characteristics.

Acknowledgements

Not applicable.

Abbreviations

Controlled Before After

Cluster-Randomised Controlled Trial

Integrated quality Criteria for the Review Of Multiple Study designs

Not Controlled Before After

Non-Cotrolled Interrupted Time Series

Organisation for Economic Cooperation and Development

Open Science Framework

Preferred Reporting Items for Systematic Review and Meta-Analyses

Randomised Controlled Trial

Search strategies

Scopus : (TITLE ({job} OR {work} OR “worker*” OR {personnel} OR {staff} OR {professionals} OR {performance}) AND TITLE ({healthcare} OR {health-care} OR doctor* OR nurse* OR {nursing} OR hospital* OR physician*) AND TITLE-ABS-KEY ({task performance} OR {contextual performance} OR {adaptive performance} OR {counterproductive} OR {counter-productive}).

Pubmed : (“Work Performance”[Mesh] OR work performance[tiab] OR job performance[tiab]) AND (Task*[tiab] OR Contextual[tiab] OR Adaptive[tiab] OR Counterproductive[tiab] OR counter-productive[tiab]) AND (“Health Personnel”[Mesh] OR health personnel[tiab] OR healthcare personnel[tiab] OR care personnel[tiab] OR care worker*[tiab] OR healthcare provider*[tiab] OR care provider*[tiab] OR healthcare worker*[tiab] OR caregiver*[tiab] OR medical staff[tiab] OR hospital staff[tiab] OR hospital personnel[tiab] OR nurse[tiab] OR nurses[tiab] OR doctor*[tiab] OR physician*[tiab]).

Web of Science : TS = (job OR work OR worker* OR personnel OR staff OR professionals) AND TS = (“task performance” OR “contextual performance” OR “adaptive performance” OR “counterproductive behavio$r”) AND TS = (care OR healthcare OR doctor* OR nurse* OR hospital* OR physician*).

Google Books : “job|work performance” “Task|contextual|adaptive performance”|Counterproductive intitle:healthcare|care|doctors|nurses|hospital|physicians intitle:performance|teamwork|competency|job|work|potential|professional|skill|behavior|behaviour.

Articles referred to in Tables  1 , 4 , 5 and 6

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19. Yuan, Y., & Zhong, S. (2014). The compensation plan on doctors considering the contextual performance. Advances in Intelligent Systems and Computing . Business School, Sichuan University, Chengdu, 610,065, China.

20. Yun, S., Kang, J., Lee, Y.-O., & Yi, Y. (2014). Work Environment and workplace bullying among Korean intensive care unit nurses. Asian Nursing Research , 8 (3), 219–225.

21. Weigl, M., Hoffmann, F., Mueller, A., Barth, N., & Angerer, P. (2014). Hospital paediatricians’ workflow interruptions, performance, and care quality: a unit-based controlled intervention. Europena Journal of Pediatrics , 173 (5), 637–645.

22. Weigl, M., Mueller, A., Sevdalis, N., & Angerer, P. (2013). Relationships of Multitasking, Physicians’ Strain, and Performance: An Observational Study in Ward Physicians. Journal of Patient Safety , 9 (1), 18–23.

23. Estes, B. C. (2013). Abusive Supervision and Nursing Performance. Nursing Forum , 48 (1), 3–16.

24. Douglas, S., Cartmill, R., Brown, R., Hoonakker, P., Slagle, J., Schultz Van Roy, K., … Carayon, P. (2013). The work of adult and pediatric intensive care unit nurses. Nursing Research , 62 (1), 50–58.

25. Anderson, D. R., St. Hilaire, D., & Flinter, M. (2012). Primary care nursing role and care coordination: An observational study of nursing work in a community health center. Online Journal of Issues in Nursing , 17 (2).

26. Qian, S.-Y., Yu, P., Zhang, Z.-Y., Hailey, D. M., Davy, P. J., & Nelson, M. I. (2012). The work pattern of personal care workers in two Australian nursing homes: A time-motion study. BMC Health Services Research , 12 (1).

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Overview Journals of publications

Authors’ contributions

The work has been drafted by MK. MK also carried out the selection of the studies. LT and FS have reviewed the content of the work throughout the process. In addition, LT gave advice on methods that are most suitable for conducting a systematic review. LT also pointed out the tools to assess the methodological quality of the studies. MK carried out these assessments. In addition to the substantive review, FS has brought structure into the article. MK, LT and FS discussed the results and implications. All the authors have read and approved the manuscript.

This review has been realized without funding.

Availability of data and materials

Data is available at https://osf.io/xn9r4/?view_only=aa9cf6c701644e1bac7bc30d853877be

Declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Marcel Krijgsheld, Email: [email protected].

Lars G. Tummers, Email: [email protected]

Floortje E. Scheepers, Email: [email protected]

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Associations between Personality Traits and Areas of Job Satisfaction: Pay, Work Itself, Security, and Hours Worked

Antonio malvaso.

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Correspondence: [email protected]

Received 2023 May 2; Revised 2023 May 19; Accepted 2023 May 24; Collection date 2023 Jun.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ).

Although studies have widely explored the connections between personality traits and job satisfaction, less is known about how personality relates to aspects of job satisfaction. The objective of this study was to explore the relationships between personality traits and various areas of job satisfaction, including pay, work, security, and hours worked. This study used ordinal regressions to analyze data from 6962 working individuals from the British Household Panel Survey (BHPS). The results showed that Neuroticism consistently has a negative association with all aspects of job satisfaction, whereas Agreeableness and Conscientiousness have positive associations with job satisfaction. Extraversion had a weak negative association with satisfaction with total pay. These findings imply that personality may play a crucial role in shaping areas of job satisfaction.

Keywords: personality, Big Five, job satisfaction, pay satisfaction

1. Introduction

The Five-Factor Model of Personality, commonly referred to as the Big Five, is comprised of five dimensions: Agreeableness, Extraversion, Conscientiousness, Openness, and Neuroticism [ 1 ]. Extraversion is characterized by assertiveness and sociability, whereas Agreeableness is associated with politeness and cooperativeness. Conscientious individuals are known for their organizational skills and task-oriented focus, while those high in Neuroticism are prone to experiencing negative emotions. Open individuals have broad interests and prefer novelty over routine. Research has demonstrated that the Big Five model comprehensively encompasses fundamental individual differences, and other personality models can be framed and understood within the Big Five framework [ 2 ]. The field of organizational psychology has demonstrated increasing interest in the construct of job satisfaction (e.g., [ 3 , 4 ]). Job satisfaction encompasses cognitive, affective, and behavioral dimensions [ 5 ], and is often defined as a positive evaluative state about one’s job, expressing contentment and positive emotions towards work [ 6 ].

Numerous studies have linked job satisfaction to various workplace characteristics. In a meta-analysis, Judge et al. [ 7 ] identified a positive association between job satisfaction and job performance. Other studies have reported that satisfied employees engage in more productive work behaviors [ 8 ] and are characterized by lower rates of absenteeism [ 9 , 10 ]. Job satisfaction has also been negatively associated with stress [ 11 , 12 , 13 ], substance use [ 14 , 15 ], and positively related to marital satisfaction, as well as mental and physical health [ 16 ]. The concept of wellbeing, which encompasses satisfaction with both work and life, has been identified as critical for maintaining an effective workforce [ 17 ]. These findings highlight the importance of identifying factors that contribute to job satisfaction and why it remains a central focus of organizational psychology research [ 18 , 19 ].

There are several theories that can account for the link between personality traits and job satisfaction as well as the need to study the associations between personality and job satisfaction. One theory is the person–environment fit theory, which suggests that job satisfaction arises when there is a good match between one’s personality and the demands of their job [ 20 , 21 , 22 ]. In other words, individuals have a higher likelihood to be satisfied with their jobs if the work environment aligns with their personality traits. For example, an extroverted person might be more satisfied in a job that involves social interaction, while an introverted person might prefer a quieter, more solitary work environment. Another theory is the self-regulation theory, which suggests that personality traits such as Conscientiousness and emotional stability are important predictors of job satisfaction because they influence an individual’s ability to manage their own behavior and emotions in the workplace [ 23 , 24 ]. Conscientious people are more likely to be organized, dependable, and able to manage their workload effectively, which can lead to greater job satisfaction. Similarly, individuals who are high in emotional stability are better able to handle stress and cope with job-related challenges, which can also lead to greater job satisfaction. Finally, the social learning theory suggests that personality traits can be shaped and influenced by social and environmental factors, such as feedback from coworkers and supervisors, as well as the overall culture of the workplace [ 25 ]. In this theory, job satisfaction is seen as a product of the interaction between an individual’s personality traits and the social context in which they work.

Although how personality traits are related to job satisfaction is widely studied [ 4 , 19 , 24 , 26 , 27 , 28 ], it remains unclear how personality traits may relate to facets of job satisfaction, including total pay, security, work itself, and hours worked. Understanding how personality traits are related to different areas of job satisfaction can also help individuals make informed decisions about career paths and job choices that align with their personality and increase the likelihood of job satisfaction [ 26 ], given that areas of job satisfaction contribute to overall job satisfaction. For example, a person who values a work–life balance may prioritize that area of job satisfaction more highly than someone who values job security above all else, which may depend on one’s personality.

Thus, the aim of the current research is to look at how personality traits contribute to facets of job satisfaction, including total pay, security, work itself, and hours worked. This study hypothesizes that Neuroticism is negatively related to areas of job satisfaction whereas other personality traits are positively related to job satisfaction [ 4 , 19 , 26 , 27 , 28 , 29 , 30 ]. However, the patterns of these associations are diverse and may be dependent on specific dimensions of job satisfaction.

The present investigation employed data from Wave 15 of the British Household Panel Survey (BHPS), which is a nationally representative survey of UK households that has been conducted annually since 1991 [ 31 ]. The data collection procedures for the British Household Panel Survey (BHPS) involve a multistage stratified sampling design to ensure representative household selection. The initial sample was drawn in 1991, with subsequent waves including panel members and newly added households. Recruitment combines random probability sampling and volunteer participation. The baseline survey gathers comprehensive demographic and socioeconomic information through face-to-face interviews with household members. Follow-up surveys, conducted annually or biennially, use standardized questionnaires to capture longitudinal data on various topics. Computer-assisted interviewing techniques are employed, with interviewers visiting households or conducting interviews via phone or online platforms. Ethical guidelines ensure participant confidentiality and informed consent. Quality control measures include interviewer training and supervision, data validation checks, and thorough documentation of procedures and variable definitions. The data collection procedures for the study have been approved by the University of Essex Ethics Committee, and all participants provided informed consent before taking part in the study. This dataset can be accessed via https://www.iser.essex.ac.uk/bhps (accessed on 1 April 2023).

The study included participants who met the following criteria: (a) were working for an employer (those who were self-employed were excluded), (b) were within the employable age range (16–65 years), and (c) provided complete data on areas of job satisfaction, personality, and demographics. Thus, in total 6962 participants remained in the current analysis.

2.2. Measures

2.2.1. areas of job satisfaction.

Participants were asked to indicate how satisfied they were with areas of job satisfaction ad hoc (each begins with “I’m going to read out a list of various aspects of jobs, and after each one I’d like you to tell me from this card (E3) which number best describes how satisfied or dissatisfied you are with that particular aspect of your own present job”). Areas of job satisfaction include “The total pay, including any overtime or bonuses”, “Your job security”, “The actual work itself”, and “The hours you work”. Participants responded on a scale from 1 (“not satisfied at all”) to 7 (“completely satisfied”).

2.2.2. Personality Traits

In this study, personality traits were assessed using a fifteen-item questionnaire based on the five-factor model of personality (BFI-S; [ 32 ]). The questionnaire consisted of three questions for each of the five personality dimensions, which were scored using a Likert scale ranging from 1 (“does not apply to me”) to 7 (“applies to me perfectly”). The mean score averaged across items was used to represent each trait. Internal consistency was evaluated using Cronbach’s alpha, and the values for each trait were: Neuroticism = 0.69, Openness = 0.66, Extraversion = 0.60, Agreeableness = 0.57, and Conscientiousness = 0.54). Previous studies have also demonstrated the reliability of this short questionnaire through test–retest correlations as well as its convergent and discriminant validity [ 33 , 34 , 35 ].

2.2.3. Control Variables

Control variables included age, sex, education, occupation, marital status, annual income, hours per week, and overtime per week. Please refer to Table 1 . for the coding of these variables.

The descriptive statistics for variables of interests in this study. Mean and standard deviation (SD) are reported for continuous variables, whereas count and percentage were reported for categorical variables.

2.3. Statistical Analysis

Due to the skewness of the outcome variables, we decided to run four ordinal logistic regression models to analyze the associations between personality traits and areas of life satisfaction. Specifically, the predictors in these models consisted of personality traits, including Agreeableness, Extraversion, Conscientiousness, Openness, and Neuroticism; and the control variables including age, sex, education, occupation, marital status, annual income, hours per week, and overtime per week. While the predictors were the same for each logistic regression model, the outcome variables for these logistic regression models were job satisfaction for total pay; job satisfaction for security; job satisfaction for work itself; and job satisfaction for hours worked, respectively. We carried out all analyses using MATLAB 2018a.

Descriptive statistics are shown in Table 1 . Results indicated that Neuroticism (OR = 0.82, 95% CI [0.78, 0.86], p <0.001) and Extraversion (OR = 0.95, 95% CI [0.90, 0.99], p < 0.05) have negative associations with job satisfaction for total pay, whereas Agreeableness (OR = 1.08, 95% CI [1.03, 1.14], p < 0.01) and Conscientiousness (OR = 1.07, 95% CI [1.02, 1.12], p < 0.01) were positively associated with job satisfaction for total pay. Openness (OR = 0.99, 95% CI [0.94, 1.04], p = 0.68) had no association with job satisfaction for total pay ( Table 2 ).

The results of ordinal logistic regressions using control variables and personality traits as predictors to predict aspects of job satisfaction including A. job satisfaction for total pay, B. job satisfaction for security, C. job satisfaction for work itself, and D. job satisfaction for hours worked.

Similarly, higher levels of Neuroticism were related to lower levels of job satisfaction for security (OR = 0.82, p < 0.01, 95% CI [0.78, 0.86]). In contrast, higher levels of Agreeableness (OR = 1.13, p < 0.01, 95% CI [1.07, 1.18]) and Conscientiousness (OR = 1.16, p < 0.01, 95% CI [1.11, 1.22]) were associated with higher levels of job satisfaction for security. Openness and Extraversion were not significantly associated with job satisfaction for security ( p > 0.05; Table 2 ).

Moreover, higher levels of Agreeableness (OR = 1.17, 95% CI [1.11, 1.23]), Conscientiousness (OR = 1.19, 95% CI [1.14, 1.26]), and lower levels of Neuroticism (OR = 0.78, 95% CI [0.74, 0.82]) were associated with higher odds of job satisfaction for work itself. Openness (OR = 1.04, 95% CI [0.99, 1.10]) and Extraversion (OR = 1.03, 95% CI [0.98, 1.08]) were not significantly related to job satisfaction for work itself ( Table 2 ).

Finally, Neuroticism had a negative association with job satisfaction for hours worked (OR = 0.82, p < 0.001, 95% CI [0.79, 0.86]). Agreeableness (OR = 1.17, p < 0.001, 95% CI [1.11, 1.23]) and Conscientiousness (OR = 1.10, p < 0.001, 95% CI [1.05, 1.16]) were positively associated with job satisfaction for hours worked. Openness (OR = 1.03, p = 0.30, 95% CI [0.98, 1.08]) and Extraversion (OR = 0.97, p = 0.14, 95% CI [0.92, 1.01]) were not significantly associated with job satisfaction for hours worked ( Table 2 ).

4. Discussion

Our aim was to investigate the associations between the Big Five and areas of job satisfaction including pay, work itself, security, and hours worked. Results revealed that Neuroticism is a consistent negative predictor of all aspects of job satisfaction, whereas Agreeableness and Conscientiousness consistently have a positive association with aspects of job satisfaction. Extraversion had a weak negative association with satisfaction with total pay. These results may indicate that if one aspect of personality traits has an association with a certain aspect of job satisfaction, then it perhaps would be similarly associated with other aspects of job satisfaction.

Neuroticism represents a personality disposition marked by a proclivity to encounter unfavorable affective states, including anxiety, fear, and worry. The negative associations found between Neuroticism and areas of job satisfaction seemed to be consistent in studies regarding the association between Neuroticism and overall job satisfaction [ 4 , 19 , 26 , 28 , 29 , 36 ]. One possible explanation for this negative relationship is that neurotic individuals have a more negative perception of their work environment, leading to lower levels of job satisfaction. Additionally, people high in Neuroticism may experience more stress and anxiety in response to work-related challenges, which can also impact job satisfaction.

Agreeableness is the trait that inclines individuals to be cooperative, empathetic, and compassionate toward others. Empirical evidence consistently supports that individuals scoring high in Agreeableness are more likely to express higher levels of job satisfaction (e.g., [ 4 , 19 , 26 , 28 , 29 , 36 ]) and achieve satisfaction in comparison to those who score low in Agreeableness. One possible explanation for this relationship is that agreeable individuals tend to have better interpersonal relationships with coworkers and supervisors, leading to more positive work experiences and greater job satisfaction. Agreeable individuals may tend to engage in pro-social behaviors such as helping others and resolving conflicts, which can lead to a more positive work environment. Another explanation is that agreeable individuals may be more likely to perceive their work as meaningful and fulfilling, leading to greater job satisfaction. Agreeable individuals may derive satisfaction from contributing to the well-being of others or making a positive impact on their work environment.

Conscientiousness is a personality trait characterized by being responsible, reliable, and organized, among other attributes. Research has found that individuals high in Conscientiousness tend to experience higher levels of job satisfaction [ 4 , 19 , 26 , 28 , 29 , 36 ]. This is likely because conscientious individuals tend to approach their work in a diligent and committed manner, which can lead to feelings of accomplishment and satisfaction when tasks are completed successfully [ 37 ]. Additionally, there were links between Conscientiousness and various job-related behaviors, such as task performance, job performance, and organizational citizenship behaviors [ 27 , 38 ], which can also contribute to higher levels of job satisfaction.

Finally, Extraversion had a weak negative association with satisfaction with total pay. Extraversion is a personality trait characterized by sociability, assertiveness, and positive emotions. Research has found that Extraversion is negatively associated with job satisfaction [ 19 , 26 , 28 , 29 , 36 ]. One explanation for this finding is that highly extraverted individuals are more likely to be motivated by social rewards such as recognition, social status, and social interactions rather than material rewards such as pay [ 24 ]. As a result, they may prioritize social rewards when evaluating their job satisfaction. Another explanation is that highly extraverted individuals may have higher expectations for their pay, due to their self-confidence and assertiveness [ 24 , 39 ]. When these expectations are not met, they may experience lower satisfaction with their pay.

This study controlled several factors, including income, hours worked, and overtime. However, some personality traits were still significant after controlling for these factors, which may indicate that job satisfaction cannot be fully explained by their actual conditions [ 40 ], but may also be determined by individual differences in terms of individual perception. These findings are consistent with previous findings that observe that the actual job condition has rather small associations with job satisfaction [ 40 ].

Several shortcomings of this research should be noted. First, aspects of job satisfaction are not limited to satisfaction with pay, security, work itself, and hours worked, but include other aspects such as working environment, social relationships with colleagues, and career development. Thus, it would be important for future studies to assess how the Big Five may be associated with other aspects of job satisfaction. Second, the use of self-reported data has limitations and challenges in research. Self-report assessments are subject to biases, including social desirability bias, memory bias, and acquiescence bias. Moreover, self-reported data might not always be accurate or reliable as it is influenced by the participant’s subjective perception and interpretation of the questions. Therefore, future studies should use multiple methods of data collection to triangulate findings and reduce potential biases. Third, although this study controlled for occupation, the relationships between the Big Five and areas of job satisfaction may depend on occupation [ 19 ]. Future research should test if occupation moderates the connections between the Big Five and areas of job satisfaction. Finally, some effects were near the threshold of 0.05, and the data were quite old; thus, interpreting these results must be accompanied with caution.

In conclusion, this study provides evidence that personality traits are related to various aspects of job satisfaction. Neuroticism is a consistent negative predictor of job satisfaction, while Agreeableness and Conscientiousness consistently have a positive association with job satisfaction. Extraversion had a weak negative association with satisfaction with pay. The patterns of these associations seem to be mostly unitary rather than diverse. Our findings suggest that personality may play a crucial role in shaping job satisfaction. Organization could use results for selecting employees. For instance, they may be interested in hiring employees with low Neuroticism scores but high Agreeableness scores, as they tend to be more satisfied with aspects of their job, which then may lead to better job performance [ 36 ].

Author Contributions

W.K.: conceptualization, data curation, formal analysis, investigation, methodology, project administration, re-sources, software, supervision, writing—original draft, and writing—review and editing. A.M.: writing—original draft and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Ethics approval was received from the University of Essex Ethics Committee.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Publicly available datasets were analyzed in this study. These data can be found here: https://www.iser.essex.ac.uk/bhps (accessed on 1 April 2023).

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This research received no external funding.

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Research: What Do People Need to Perform at a High Level?

• Zorana Ivcevic,
• Robin Stern,
• Andrew Faas

A look at the personal, relational, and organizational factors that will help your team do its best work.

The researchers identified common factors helping (or hurting) employee effectiveness. Among them were personal factors including mindsets and skills that the individual brings to the job and over which they have some control. Others are managed at an organizational level: managing rules about how work is done and showing emotional intelligence. Turning to nurses during the Covid-19 pandemic, the researchers found that these factors became even more important during crisis. They found that when the background uncertainty and anxiety are high, if the organization does not provide clear expectations and the supervisors do not acknowledge staff feelings and help them manage them, workers will not be able to work to their full potential.

Of practical importance to organizational leaders, our research provides insight about how managers can boost employees’ potential – even in times of crisis.

Even before the Covid-19 pandemic, American workers were struggling to reach their full potential. In a national survey we conducted of more than 14,500 workers across industries in 2017, approximately 85% of them said they were not working at 100% of their potential. In fact, only 15% of workers said they were. Moreover, 16% said they were using less than 50% of their potential. What was keeping the vast majority of workers from using all of their potential? And what was empowering the minority who reported that they were able to do so?

• ZI Zorana Ivcevic , Ph.D., is a Senior Research Scientist at the Yale Center for Emotional Intelligence and the Director of the Emotions in the Workplace initiative. In her research, Dr. Ivcevic focuses on the role of emotions and emotional intelligence for well-being and performance.
• Robin Stern , Ph.D. is associate director of the Yale Center for Emotional Intelligence, co-developer of RULER, an evidence-based approach to social and emotional learning, and a psychoanalyst in private practice.
• AF Andrew Faas is a management consultant, author, and philanthropist. He is the founder of the Faas Foundation, founder and Co-CEO of Accordant Advisors, and writer on psychological safety in the workplace.

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Employee psychological well-being and job performance: exploring mediating and moderating mechanisms

International Journal of Organizational Analysis

ISSN : 1934-8835

Article publication date: 12 August 2020

Issue publication date: 7 May 2021

Given the importance of employee psychological well-being to job performance, this study aims to investigate the mediating role of affective commitment between psychological well-being and job performance while considering the moderating role of job insecurity on psychological well-being and affective commitment relationship.

Design/methodology/approach

The data were gathered from employees working in cellular companies of Pakistan using paper-and-pencil surveys. A total of 280 responses were received. Hypotheses were tested using structural equation modeling technique and Hayes’s Model 1.

Findings suggest that affective commitment mediates the association between psychological well-being (hedonic and eudaimonic) and employee job performance. In addition, perceived job insecurity buffers the association of psychological well-being (hedonic and eudaimonic) and affective commitment.

Practical implications

The study results suggest that fostering employee psychological well-being may be advantageous for the organization. However, if interventions aimed at ensuring job security are not made, it may result in adverse employee work-related attitudes and behaviors.

Originality/value

The study extends the current literature on employee well-being in two ways. First, by examining psychological well-being in terms of hedonic and eudaimonic well-being with employee work-related attitude and behavior. Second, by highlighting the prominent role played by perceived job insecurity in explaining some of these relationships.

• Psychological well-being
• Affective commitment
• Job insecurity
• Job performance
• Eudaimonic wellbeing
• Hedonic wellbeing

Kundi, Y.M. , Aboramadan, M. , Elhamalawi, E.M.I. and Shahid, S. (2021), "Employee psychological well-being and job performance: exploring mediating and moderating mechanisms", International Journal of Organizational Analysis , Vol. 29 No. 3, pp. 736-754. https://doi.org/10.1108/IJOA-05-2020-2204

Emerald Publishing Limited

Copyright © 2020, Yasir Mansoor Kundi, Mohammed Aboramadan, Eissa M.I. Elhamalawi and Subhan Shahid.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Does the employee well-being have important implications both at work and for other aspects of an employees’ life? Of course! For years, we have known that they impact life at work and a plethora of research has examined the impact of employee well-being on work outcomes (Karapinar et al. , 2019 ; Turban and Yan, 2016 ). What is less understood is how employee well-being impacts job performance. Evidence suggests that employee health and well-being are among the most critical factors for organizational success and performance (Bakker et al. , 2019 ; Turban and Yan, 2016 ). Several studies have documented that employee well-being leads to various individual and organizational outcomes such as increased organizational performance and productivity (Hewett et al. , 2018 ), customer satisfaction (Sharma et al. , 2016 ), employee engagement (Tisu et al. , 2020 ) and organizational citizenship behavior (OCB; Mousa et al. , 2020 ).

The organizations’ performance and productivity are tied to the performance of its employees (Shin and Konrad, 2017 ). Much evidence has shown the value of employee job performance (i.e. the measurable actions, behaviors and outcomes that employee engages in or bring about which are linked with and contribute to organizational goals; Viswesvaran and Ones, 2017 ) for organizational outcomes and success (Al Hammadi and Hussain, 2019 ; Shin and Konrad, 2017 ), which, in turn, has led scholars to seek to understand what drives employee performance. Personality traits (Tisu et al. , 2020 ), job conditions and organizational characteristics (Diamantidis and Chatzoglou, 2019 ) have all been identified as critical antecedents of employee job performance.

However, one important gap remains in current job performance research – namely, the role of psychological well-being in job performance (Hewett et al. , 2018 ). Although previous research has found happy workers to be more productive than less happy or unhappy workers (DiMaria et al. , 2020 ), a search of the literature revealed few studies on psychological well-being and job performance relationship (Salgado et al. , 2019 ; Turban and Yan, 2016 ). Also, very little is known about the processes that link psychological well-being to job performance. Only a narrow spectrum of well-being related antecedents of employee performance has been considered, especially in terms of psychological well-being. Enriching our understanding of the consequences and processes of psychological well-being in the workplace, the present study examines the relationship between psychological well-being and job performance in the workplace setting. Such knowledge will not only help managers to attain higher organizational performance during the uncertain times but will uncover how to keep employees happy and satisfied (DiMaria et al. , 2020 ).

Crucially, to advance job performance research, more work is needed to examine the relationship between employees’ psychological well-being and their job performance (Ismail et al. , 2019 ). As Salgado et al. (2019) elaborated, we need to consider how an employees’ well-being affects ones’ performance at work. In an attempt to fill this gap in the literature, the present study seeks to advance job performance research by linking ones’ psychological well-being in terms of hedonic and eudaimonic well-being to ones’ job performance. Hedonic well-being refers to the happiness achieved through experiences of pleasure and enjoyment, while eudaimonic well-being refers to the happiness achieved through experiences of meaning and purpose (Huta, 2016 ; Rahmani et al. , 2018 ). We argue that employees with high levels of psychological well-being will perform well as compared to those having lower levels of psychological well-being. We connect this psychological well-being-job performance process through an employee affective commitment (employees’ perceptions of their emotional attachment to or identification with their organization; Allen and Meyer, 1996 ) – by treating it as a mediating variable between well-being-performance relationship.

Additionally, we also examine the moderating role of perceived job insecurity in the well-being-performance relationship. Perceived job insecurity refers to has been defined as the perception of being threatened by job loss or an overall concern about the continued existence of the job in the future (De Witte et al. , 2015 ). There is evidence that perceived job insecurity diminishes employees’ level of satisfaction and happiness and may lead to adverse job-related outcomes such as decreased work engagement (Karatepe et al. , 2020 ), deviant behavior (Soomro et al. , 2020 ) and reduced employee performance (Piccoli et al. , 2017 ). Thus, addressing the gap mentioned above, this study has two-fold objectives; First, to examine how the path between psychological well-being and job performance is mediated through employee affective commitment. The reason to inquire about this path is that well-being is associated with an employees’ happiness, pleasure and personal growth (Ismail et al. , 2019 ). Therefore, higher the well-being, higher will be the employees’ affective commitment, which, in turn, will lead to enhanced job performance. The second objective is to empirically test the moderating effects of perceived job insecurity on employees’ emotional attachment with their organizations. Thus, we propose that higher job insecurity may reduce the well-being of employees and their interaction may result in lowering employees’ emotional attachment with their organization.

The present study brings together employee well-being and performance literature and contributes to these research areas in two ways. First, we contribute to this line of inquiry by investigating the direct and indirect crossover from hedonic well-being and eudaimonic well-being to employees’ job performance. We propose that psychological well-being (hedonic and eudaimonic) influence job performance through employee affective commitment. Second, prior research shows that the effect of well-being varies across individuals indicating the presence of possible moderators influencing the relationship between employee well-being and job outcomes (Lee, 2019 ). We, therefore, extend the previous literature by proposing and demonstrating the general possibility that perceived job insecurity might moderate the relationship of psychological well-being (hedonic and eudaimonic) and affective commitment. While there is evidence that perceived job insecurity influence employees’ affective commitment (Schumacher et al. , 2016 ), what is not yet clear is the impact of perceived job insecurity on psychological well-being − affective commitment relationship. The proposed research model is depicted in Figure 1 .

2. Hypotheses development

2.1 psychological well-being and affective commitment.

Well-being is a broad concept that refers to individuals’ valued experience (Bandura, 1986 ) in which they become more effective in their work and other activities (Huang et al. , 2016 ). According to Diener (2009) , well-being as a subjective term, which describes people’s happiness, the fulfillment of wishes, satisfaction, abilities and task accomplishments. Employee well-being is further categorized into two types, namely, hedonic well-being and eudaimonic well-being (Ballesteros-Leiva et al. , 2017 ). Compton et al. (1996) investigated 18 scales that assess employee well-being and found that all the scales are categorized into two broad categories, namely, subjective well-being and personal growth. The former is referred to as hedonic well-being (Ryan and Deci, 2000 ) whereas, the latter is referred to as eudaimonic well-being (Waterman, 1993 ).

Hedonic well-being is based on people’s cognitive component (i.e. people’s conscious assessment of all aspects of their life; Diener et al. , 1985 ) and affective component (i.e. people’s feelings that resulted because of experiencing positive or negative emotions in reaction to life; Ballesteros-Leiva et al. , 2017 ). In contrast, eudaimonic well-being describes people’s true nature and realization of their actual potential (Waterman, 1993 ). Eudaimonic well-being corresponds to happy life based upon ones’ self-reliance and self-truth (Ballesteros-Leiva et al. , 2017 ). Diener et al. (1985) argued that hedonic well-being focuses on happiness and has a more positive affect and greater life satisfaction, and focuses on pleasure, happiness and positive emotions (Ryan and Deci, 2000 ; Ryff, 2018 ). Contrarily, eudaimonic well-being is different from hedonic well-being as it focuses on true self and personal growth (Waterman, 1993 ), recognition for ones’ optimal ability and mastery ( Ryff, 2018 ). In the past, it has been found that hedonic well-being and eudaimonic well-being are relatively correlated with each other but are distinct concepts (Sheldon et al. , 2018 ).

To date, previous research has measured employee psychological well-being with different indicators such as thriving at work (Bakker et al. , 2019 ), life satisfaction (Clark et al. , 2019 ) and social support (Cai et al. , 2020 ) or general physical or psychological health (Grey et al. , 2018 ). Very limited studies have measured psychological well-being with hedonic and eudaimonic well-being, which warrants further exploration (Ballesteros-Leiva et al. , 2017 ). Therefore, this study assesses employee psychological well-being based upon two validated measures, namely, hedonic well-being (people’s satisfaction with life in general) and eudaimonic well-being (people’s personal accomplishment feelings).

Employee well-being has received some attention in organization studies (Huang et al. , 2016 ). Prior research has argued that happier and healthier employees increase their effort, performance and productivity (Huang et al. , 2016 ). Similarly, research has documented that employee well-being has a positive influence on employee work-related attitudes and behaviors such as, increasing OCB (Mousa et al. , 2020 ), as well as job performance (Magnier-Watanabe et al. , 2017 ) and decreasing employees’ work-family conflict (Karapinar et al. , 2019 ) and absenteeism (Schaumberg and Flynn, 2017 ). Although there is evidence that employee well-being positively influences employee work-related attitudes, less is known about the relationship between psychological well-being (hedonic and eudaimonic) and employee affective commitment (Pan et al. , 2018 ; Semedo et al. , 2019 ). Moreover, the existing literature indicated that employee affective commitment is either used as an antecedent or an outcome variable of employee well-being (Semedo et al. , 2019 ; Ryff, 2018 ). However, affective commitment as an outcome variable of employee well-being has gained less scholarly attention, which warrants further investigation. Therefore, in the present study, we seek to examine employee affective commitment as an outcome variable of employee psychological well-being because employees who are happy and satisfied in their lives are more likely to be attached to their organizations (Semedo et al. , 2019 ).

Hedonic well-being positively predicts employee affective commitment.

Eudaimonic well-being positively predicts employee affective commitment.

2.2 Affective commitment and job performance

The concept of organizational commitment was first initiated by sit-bet theory in the early 1960s (Becker, 1960 ). Organizational commitment is defined as the psychological connection of employees to the organization and involvement in it (Cooper-Hakim and Viswesvaran, 2005 ). It is also defined as the belief of an individual in his or her organizational norms (Hackett et al. , 2001 ); the loyalty of an employee toward the organization (Cooper-Hakim and Viswesvaran, 2005 ) and willingness of an employee to participate in organizational duties (Williams and Anderson, 1991 ).

Organizational commitment is further categorized into three correlated but distinct categories (Meyer et al. , 1993 ), known as affective, normative and continuance. In affective commitment, employees are emotionally attached to their organization. In normative commitment, employees remain committed to their organizations due to the sense of obligation to serve. While in continuance commitment, employees remain committed to their organization because of the costs associated with leaving the organization (Allen and Meyer, 1990 , p. 2). Among the dimensions of organizational commitment, affective commitment has been found to have the most substantial influence on organizational outcomes (Meyer and Herscovitch, 2001 ). It is a better predictor of OCB (Paul et al. , 2019 ), low turnover intention (Kundi et al. , 2018 ) and job performance (Jain and Sullivan, 2019 ).

Affective commitment positively predict employee job performance.

2.3 Affective commitment as a mediator

Many studies had used the construct of affective commitment as an independent variable, mediator and moderating variable because of its importance as an effective determinant of work outcomes such as low turnover intention, job satisfaction and job performance (Jain and Sullivan, 2019 ; Kundi et al. , 2018 ). There is very little published research on employee well-being and affective commitment relationship. Surprisingly, the effects of employee psychological well-being in terms of hedonic well-being and eudaimonic well-being have not been closely examined.

Affective commitment mediates the association between hedonic well-being and job performance.

Affective commitment mediates the association between eudaimonic well-being and job performance.

2.4 The moderating role of job insecurity

Job insecurity is gaining importance because of the change in organizational structure as it is becoming flattered, change in the nature of the job as it requires a diverse skill set and change in human resource (HR) practices as more temporary workers are hired nowadays (Piccoli et al. , 2017 ; Kundi et al. , 2018 ). Such changes have caused several adverse outcomes such as job dissatisfaction (Bouzari and Karatepe, 2018 ), unethical pro-organizational behavior (Ghosh, 2017 ), poor performance (Piccoli et al. , 2017 ), anxiety and lack of commitment (Wang et al. , 2018 ).

Lack of harmony on the definition of job insecurity can be found among the researchers. However, a majority of them acknowledge that job insecurity is subjective and can be referred to as a subjective perception (Wang et al. , 2018 ). Furthermore, job insecurity is described as the perception of an employee regarding the menace of losing a job in the near future (De Witte et al. , 2015 ). When there is job insecurity, employees experience a sense of threat to the continuance and stability of their jobs (Shoss, 2017 ).

Although job insecurity has been found to influence employee work-related attitudes, less is known about its effects on behavioral outcomes (Piccoli et al. , 2017 ). As maintained by the social exchange theory, behaviors are the result of an exchange process (Blau, 1964 ). Furthermore, these exchanges can be either tangible or socio-emotional aspects of the exchange process (Kundi et al. , 2018 ). Employees who perceive and feel that their organization is providing them job security and taking care of their well-being will turn to be more committed to their organization (Kundi et al. , 2018 ; Wang et al. , 2018 ). Much research has found that employees who feel job security are happier and satisfied with their lives (Shoss, 2017 ; De Witte et al. , 2015 ) and are more committed to their work and organization (Bouzari and Karatepe, 2018 ; Wang et al. , 2018 ). Shoss (2017) conducted a thorough study on job insecurity and found that job insecurity can cause severe adverse consequences for both the employees and organizations.

Employees who are uncertain about their jobs (i.e. high level of perceived job insecurity) are less committed with their organizations.

Employees with temporary job contracts were found to have low organizational committed as compared to the employees with permanent job contracts.

Such a difference between temporary and permanent job contract holders was mainly due to the perceived job insecurity by the temporary job contract holders.

Job insecurity will moderate the relationship between hedonic well-being, eudaimonic well-being and affective organizational commitment.

3.1 Sample and procedure

The data for this study came from a survey of Pakistani employees, who worked in five private telecommunication organizations (Mobilink, Telenor, Ufone, Zong and Warid). These five companies were targeted because they are the largest and highly competitive companies in Pakistan. Moreover, the telecom sector is a private sector where jobs are temporary or contractual (Kundi et al. , 2018 ). Hence, the investigation of how employees’ perceptions of job insecurity influence their psychological well-being and its outcomes is highly relevant in this context. Studies exploring such a phenomenon are needed, particularly in the Pakistani context, to have a better insight and thereby strengthen the employee well-being and job performance literature.

Two of the authors had personal and professional contacts to gain access to these organizations. The paper-and-pencil method was used to gather the data. Questionnaires were distributed among 570 participants with a cover letter explaining the purpose of the study, noted that participation was voluntary, and provided assurances that their responses would be kept confidential and anonymous. After completion of the questionnaires, the surveys were collected the surveys on-site by one of the authors. As self-reported data often render itself to common method bias (CMB; Podsakoff et al. , 2012 ), we applied several procedural remedies such as reducing the ambiguity in the questions, ensuring respondent anonymity and confidentiality, separating of the predictor and criterion variable and randomizing the item order to limit this bias.

Of the 570 surveys distributed initially, 280 employees completed the survey form (response rate = 49%). According to Baruch and Holtom (2008) , the average response rate for studies at the individual level is 52.6% (SD = 19.7). Hence, our response rate meets the standard for a minimum acceptable response rate, which is 49%. Of the 280 respondents, 39% were female, their mean age was 35.6 years (SD = 5.22) and the average organizational tenure was 8.61 years (SD  =  4.21). The majority of the respondents had at least a bachelors’ degree (83 %). Respondents represented a variety of departments, including marketing (29%), customer services (26%), finance (20%), IT (13%) and HR (12%).

3.2 Measures

The survey was administered to the participants in English. English is the official language of correspondence for professional organizations in Pakistan (De Clercq et al. , 2019 ). All the constructs came from previous research and anchored on a five-point Likert scale ranging from 1 = Strongly disagree to 5 = Strongly agree.

Psychological well-being. We measured employee psychological well-being with two sub-dimensions, namely, hedonic well-being and eudaimonic well-being. Hedonic well-being was measured using five items (Diener et al. , 1985 ). A sample item is “my life conditions are excellent” ( α = 0.86). Eudaimonic well-being was measured using 21 items (Waterman et al. , 2010 ), of which seven items were reverse-scored due to its negative nature. Sample items are “I feel that I understand what I was meant to do in my life” and “my life is centered around a set of core beliefs that give meaning to my life” ( α = 0.81).

Affective commitment. The affective commitment was measured using a six-item inventory developed by Allen and Meyer (1990) . The sample items are “my organization inspires me to put forth my best effort” and “I think that I will be able to continue working here” ( α = 0.91).

Job insecurity. Job insecurity was measured using a five-item inventory developed by Chirumbolo et al. (2015) . The sample item is “I fear I will lose my job” ( α = 0.87).

Job performance . We measured employee job performance with the seven-item inventory developed by Williams and Anderson (1991) . The sample items are “I do fulfill my responsibilities, which are mentioned in the job description” and “I try to work as hard as possible” ( α = 0.87).

Controls. We controlled for respondents’ age (assessed in years), gender (1 = male, 2 = female) and organizational tenure (assessed in years) because prior research (Alessandri et al. , 2019 ; Edgar et al. , 2020 ) has found significant effects of these variables on employees’ job performance.

4.1 Descriptive statistics

Table 1 presents the means, standard deviations and correlations among study variables.

4.2 Construct validity

Before testing hypotheses, we conducted a series of confirmatory factor analyzes (CFAs) using AMOS 22.0 to examine the distinctiveness of our study variables. Following the guidelines of Hu and Bentler (1999) , model fitness was assessed with following fit indices; comparative fit index (CFI), root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR). We used a parceling technique (Little et al. , 2002 ) to ensure item to sample size ratio. According to Williams and O’Boyle (2008) , the item-parceling approach is widely used in HRM research, which allows estimation of fewer model parameters and subsequently leads to the optimal variable to sample size ratio and stable parameter estimates (Wang and Wang, 2019 ). Based on preliminary CFAs, we combined the highest item loading with the lowest item loading to create parcels that were equally balanced in terms of their difficulty and discrimination. Item-parceling was done only for the construct of eudaimonic well-being as it entailed a large number of items (i.e. 21 items). Accordingly, we made five parcels for the eudaimonic well-being construct (Waterman et al. , 2010 ).

As shown in Table 2 , the CFA results revealed that the baseline five‐factor model (hedonic well-being, eudaimonic well-being, job insecurity, affective commitment and job performance) was significant ( χ 2 = 377.11, df = 199, CFI = 0.971, RMSEA = 0.034 and SRMR = 0.044) and better than the alternate models, including a four‐factor model in which hedonic well-being and eudaimonic well-being were considered as one construct (Δ χ 2 = 203.056, Δdf = 6), a three-factor model in which hedonic well-being, eudaimonic well-being and affective commitment were loaded on one construct (Δ χ 2 = 308.99, Δdf = 8) and a one‐factor model in which all items loaded on one construct (Δ χ 2 = 560.77, Δdf = 11). The results, therefore, provided support for the distinctive nature of our study variables.

To ensure the validity of our measures, we first examined the convergent validity through the average variance extracted (AVE). We found AVE scores higher than the threshold value of 0.5 ( Table 1 ; Fornell and Larcker, 1981 ), supporting the convergent validity of our constructs. We also estimated discriminant validity by comparing the AVE of each construct with the average shared variance (ASV), i.e. mean of the squared correlations among constructs ( Hair et al. , 2010 ). As expected, all the values of AVE were higher than the ASV constructs, thereby supporting discriminant validity ( Table 1 ).

4.3 Common method variance

Harman’s one-factor test.

CFA ( Podsakoff et al. , 2012 ).

Harman’s one-factor test showed five factors with eigenvalues of greater than 1.0 accounted for 69.12% of the variance in the exogenous and endogenous variables. The results of CFA showed that the single-factor model did not fit the data well ( χ 2 = 937.88, df = 210, CFI = 0.642, RMSEA = 0.136, SRMR = 0.122). These tests showed that CMV was not a major issue in this study.

4.4 Hypotheses testing

The hypotheses pertaining to mediation were tested using a structural model in AMOS 22.0 ( Figure 2 ), which had an acceptable goodness of fit ( χ 2 = 298.01, df = 175, CFI = 0.97, RMSEA = 0.04 and SRMR = 0.04). Hypotheses about moderation were tested in SPSS (25 th edition) using PROCESS Model I ( Hayes, 2017 ; Table 3 ).

H1a and H1b suggested that hedonic well-being and eudaimonic well-being positively relate to employee affective commitment. According to Figure 2 , the results indicate that hedonic well-being ( β = 0.26, p < 0.01) and eudaimonic well-being ( β = 0.32, p < 0.01) are positively related to employee affective commitment. Taken together, these two findings provide support for H1a and H1b . In H2 , we predicted that employee affective commitment would positively associate with employee job performance. As seen in Figure 2 , employee affective commitment positively predicted employee job performance ( β = 0.41, p < 0.01), supporting H2 .

H3a and H3b suggested that employee affective commitment mediates the relationship between hedonic and eudaimonic well-being and employee job performance. According to Figure 2 , the results indicate that hedonic well-being is positively related to employee job performance via employee affective commitment ( β = 0.11, 95% CI = 0.09; 0.23). Similarly, eudaimonic well-being is positively related to employee job performance via employee affective commitment ( β = 0.15, 95% CI = 0.12; 0.35), supporting H3a and H3b .

Hedonic well-being.

Eudaimonic well-being and employee affective commitment.

In support of H4a , our results ( Table 3 ) revealed a negative and significant interaction effect between hedonic well-being and job insecurity on employee affective commitment ( β = −0.12, p < 0.05). The pattern of this interaction was consistent with our hypothesized direction; the positive relationship between hedonic well-being and employee affective commitment was weaker in the presence of high versus low job insecurity ( Figure 3 ). Likewise, the interaction effect between eudaimonic well-being and job insecurity on employee affective commitment was negatively significant ( β = −0.28, p < 0.01). The pattern of this interaction was consistent with our hypothesized direction; the positive relationship between eudaimonic well-being and employee affective commitment was weaker in the presence of high versus low job insecuritay ( Figure 4 ). Thus, H4a and H4b were supported. The pattern of these interactions was consistent with our hypothesized direction; the positive relationship of hedonic well-being and eudaimonic well-being with an employee affective commitment were weaker in the presence of high versus low perceived job insecurity.

5. Discussion

The present research examined the direct and indirect crossover from psychological well-being (hedonic and eudaimonic) to job performance through employee affective commitment and the moderating role of job insecurity between psychological well-being and affective commitment relationship. The results revealed that both hedonic well-being and eudaimonic well-being has a direct and indirect effect on employee job performance. Employee affective commitment was found to be a potential mediating mechanism (explaining partial variance) in the relationship between psychological well-being and job performance. Findings regarding the buffering role of job insecurity revealed that job insecurity buffers the positive relationship between psychological well-being and employee affective commitment such that higher the job insecurity, lower will be employee affective commitment. The findings generally highlight and reinforce that perceived job insecurity can be detrimental for both employees’ well-being and job-related behaviors (Soomro et al. , 2020 ).

5.1 Theoretical implications

The present study offers several contributions to employee well-being and job performance literature. First, the present research extends the employee well-being literature by investigating employee affective commitment as a key mechanism through which psychological well-being (hedonic and eudaimonic) influences employees’ job performance. In line with SDT, we found that both hedonic well-being and eudaimonic well-being enhanced employees’ affective commitment, which, in turn, led them to perform better in their jobs. Our study addresses recent calls for research to understand better how psychological well-being influence employees’ performance at work (Huang et al. , 2016 ), and adds to a growing body of work, which confirms the importance of psychological well-being in promoting work-related attitudes and behaviors (Devonish, 2016 ; Hewett et al. , 2018 ; Ismail et al. , 2019 ). Further, we have extended the literature on employee affective commitment, highlighting that psychological well-being is an important antecedent of employee’ affective commitment and thereby confirming previous research by Aboramadan et al. (2020) on the links between affective commitment and job performance.

Second, our results provide empirical support for the efficacy of examining the different dimensions of employee well-being, i.e. hedonic well-being and eudaimonic well-being as opposed to an overall index of well-being at work. Specifically, our results revealed that both hedonic well-being and eudaimonic well-being boost both employees’ attachment with his or her organization and job performance (Hewett et al. , 2018 ; Luu, 2019 ). Among the indicators of psychological well-being, eudaimonic well-being (i.e. realization and fulfillment of ones’ true nature) was found to have more influence on employee affective commitment and job performance as compared to hedonic well-being (i.e. state of happiness and sense of flourishing in life). Therefore, employees who experience high levels of psychological well-being are likely to be more attached to their employer, which, in turn, boosts their job performance.

Third, job insecurity is considered as an important work-related stressor (Schumacher et al. , 2016 ). However, the moderating role of job insecurity on the relationship between psychological well-being and affective commitment has not been considered by the previous research. Based on social exchange theory (Blau, 1964 ), we expected job insecurity to buffer the positive relationship between the psychological well-being and affective commitment. The results showed that employees with high levels of perceived job insecurity reduce the positive relationship of psychological well-being (hedonic and eudaimonic) and affective commitment. This finding is consistent with previous empirical evidence supporting the adverse role of perceived job insecurity in reducing employees’ belongingness with their organization (Jiang and Lavaysse, 2018 ). There is strong empirical evidence (Qian et al. , 2019 ; Schumacher et al. , 2016 ) that employee attitudes and health are negatively affected by increasing levels of job insecurity. Schumacher et al. (2016) suggested in an elaborate explanation of the social exchange theory that the constant worrying about the possibility of losing ones’ job promotes psychological stress and feelings of unfairness, which, in turn, affects employees’ affective commitment. Hence, employees’ psychological well-being and affective commitment are heavily influenced by the experience of high job insecurity.

5.2 Practical implications

Our study has several implications. First and foremost, this study will help managers in understanding the importance of employees’ psychological well-being for work-related attitudes and behavior. Based on our findings, managers need to understand how important psychological well-being is for employees’ organizational commitment and job performance. According to Hosie and Sevastos (2009) , several human resource-based interventions could foster employees’ psychological well-being, such as selecting and placing employees into appropriate positions, ensuring a friendly work environment and providing training that improves employees’ mental health and help them to manage their perceptions positively.

Besides, managers should provide their employees with opportunities to use their full potential, which will increase employees’ sense of autonomy and overall well-being (Sharma et al. , 2017 ). By promoting employee well-being in the workplace, managers can contribute to developing a workforce, which will be committed to their organizations and will have better job performance. However, based on our findings, in the presence of job insecurity, organizations spending on interventions to improve employees’ psychological well-being, organizational commitment and job performance might go in vain. In other words, organizations should ensure that employees feel a sense of job security or else the returns on such interventions could be nullified.

Finally, as organizations operate in a volatile and highly competitive environment, it is and will be difficult for them to provide high levels of job security to their employees, especially in developing countries such as Pakistan (Soomro et al. , 2020 ). Given the fact that job insecurity leads to cause adverse employee psychological well-being and affective commitment, managers must be attentive to subordinates’ perceptions of job insecurity and adverse psychological well-being and take action to prevent harmful consequences (Ma et al. , 2019 ). Organizations should try to avoid downsizings, layoffs and other types of structural changes, respectively, and find ways to boost employees’ perceptions of job security despite those changes. If this is not possible, i.e. the organization not able to provide job security, this should be communicated to employees honestly and early.

5.3 Limitations and future studies

There are several limitations to this study. First, we measured our research variables by using a self-report survey at a single point of time, which may result in CMB. We used various procedural remedies to mitigate the potential for CMB and conducted CFA as per the guidelines of Podsakoff et al. (2012) to ensure that CMV was unlikely to be an issue in our study. However, future research may rely on supervisors rated employees’ job performance or collect data at different time points to avoid the threat of such bias.

Second, the sample of this study consisted of employees working in cellular companies of Pakistan with different demographic characteristics and occupational backgrounds; thus, the generalizability of our findings to other industries or sectors is yet to be established. Future research should test our research model in various industries and cultures.

A final limitation pertains to the selection of a moderating variable. As this study was conducted in Pakistan, contextual factors such as the perceived threat to terrorism, law and order situation or perceived organizational injustice might also influence the psychological well-being of employees working in Pakistan (Jahanzeb et al. , 2020 ; Sarwar et al. , 2020 ). Future studies could consider the moderating role of such external factors in the relationship between employee psychological well-being, affective commitment and job performance.

6. Conclusion

This study proposed a framework to understand the relationship between employee psychological well-being, affective commitment and job performance. It also described how psychological well-being influences job performance. Additionally, this study examined the moderating role of perceived job insecurity on psychological well-being and affective commitment relationship. The results revealed that employee psychological well-being (hedonic and eudaimonic) has beneficial effects on employee affective commitment, which, in turn, enhance their job performance. Moreover, the results indicated that perceived job insecurity has ill effects on employee affective commitment, especially when the employee has high levels of perceived job insecurity.

Research model

Structural model with standardized coefficients; N = 280

Interactive effect of hedonic well-being and job insecurity on employee affective commitment

Interactive effect of eudaimonic well-being and job insecurity on employee affective commitment

Descriptive statistics and correlations among of variables

* p < 0.05,

** p < 0.01; Unstandardized coefficients and average bootstrap estimates are stated; demographic variables are controlled; bootstrapping procedure [5,000 iterations, bias-corrected, 95% CI]

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Further reading

Sabella , A.R. , El-Far , M.T. and Eid , N.L. ( 2016 ), “ The effects of organizational and job characteristics on employees' organizational commitment in arts-and-culture organizations ”, International Journal of Organizational Analysis , Vol. 24 No. 5 , pp. 1002 - 1024 .

Acknowledgements

Funding and Support statement : The authors did not receive any external funding or additional support from third parties for this work.

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What does research tell us about depression, job performance, and work productivity?

Affiliation.

• 1 Institute for Clinical Research and Health Policy Studies, Tufts-New England Medical Center, Boston, MA 02111, USA. [email protected]
• PMID: 18404013
• DOI: 10.1097/JOM.0b013e31816bae50

Objective: To assess the work impact of depression.

Methods: A review of research articles published since 2002, reporting on the magnitude and/or nature of depression's impact on work.

Results: This research is characterized by the use of three outcome indicators (employment status, absenteeism, and presenteeism metrics) and three research designs (population-based, workplace, and clinical). The literature documents that, compared to non-depressed individuals, those with depression have more unemployment, absences, and at-work performance deficits. Methodological variation makes it difficult to determine the magnitude of these differences. Additionally, the research suggests that the work impact of depression is related to symptom severity and that symptom relief only partly reduces the adverse work outcomes of depression.

Conclusions: Research has contributed to knowledge of the multidimensional work impact of depression. Further developing intervention research is an important next step.

Publication types

• Research Support, N.I.H., Extramural
• Absenteeism
• Depression*
• Task Performance and Analysis*
• Workplace / psychology*

Grants and funding

• T32 MH019733-14/MH/NIMH NIH HHS/United States