You get what you expect: assessing the effect of a compressed work schedule on time pressure, fatigue, perceived productivity, and work-life balance

ABSTRACT

With a refreshed surge of interest in alternative work schedules, such as the compressed workweek, as a tool to increase employer attractiveness, the question of the effects of such work arrangements recurs. In light of inconsistent research findings, we examined the effects of the implementation of a four-day compressed workweek on time pressure, fatigue, perceived productivity, and work-life balance within a construction company. Moreover, we investigated the effect of employee expectations. Drawing on longitudinal data (N = 247) and using Latent Change Score Modelling (LCSM), we found that work-life balance increased while fatigue and time pressure decreased. Three months after implementing a compressed work schedule, perceived productivity remained stable. These effects were contingent on individuals’ expectations regarding the effect of the compressed schedule. The results highlight the importance of employee expectations in shaping the outcomes of the adoption of alternative work schedules.

KEYWORDS:

• Four-day workweek
• compressed workweek
• alternative work schedules
• work-life balance
• expectancy effect

Introduction

Staff shortages are one of the main obstacles to hiring new staff (Savsek, 2018). As this issue will likely be exacerbated by the impending wave of retirement and the significant decrease in the overall number of people of working age (Statistik Austria, 2022), employers are worried about a looming shortage of skilled workers or already struggle with retaining skilled staff and attracting prospective employees. Moreover, certain industries, such as the hospitality, wood, and construction industries (Mohr, 2022), face unique challenges in attracting and retaining skilled workers due to poor working conditions, bad industry reputation, and inadequate training opportunities.

In response, many companies are exploring strategies to attract and retain suitable employees. One strategy is implementing alternative work arrangements, such as compressed work schedules (Wadsworth & Facer, 2016). The compressed workweek is characterized by work hours being spread across fewer days, with the most popular option being the 4/10 week, i.e., working ten hours on four days each (Feeney & Stritch, 2019). While this limits free time during the workweek, individuals often save time commuting and gain an extra free day, which can be used for leisure, life administration, or recovery (Veal, 2023).

Research findings regarding the effects of compressed schedules remain inconsistent (e.g., Baltes et al., 1999; Bell et al., 2015; Cette et al., 2011; Facer & Wadsworth, 2008; Spicer & Lyons, 2023; Wadsworth et al., 2010; Wright et al., 2013) and research often lacks methodological rigour (Campbell, 2023). The present study draws on longitudinal data to assess the effects of a compressed work schedule in a construction company. We, moreover, move beyond the study of the consequences of compressed work schedules and explore the role of employees’ expectations as a potential driver of beneficial effects, thus adding insights into factors that may contribute to the effective implementation of compressed work schedules.

The compressed workweek: conceptual framework and implications

Early research into the effects of compressed work schedules often lacked a comprehensive theoretical framework (Armstrong-Stassen, 1998; Baltes et al., 1999) and no single framework has emerged since. Research, however, most frequently relies on the theory of work adjustment (Dawis et al., 1968). It conceptualized work as an interaction between individuals and their work environments. Work adjustment refers to the degree of correspondence between job requirements and employees’ abilities, needs, and preferences and contributes to employee satisfaction. Researchers (e.g., Baltes et al., 1999; Deery et al., 2017; Pierce & Dunham, 1992) have argued that the compressed work schedule can enhance this correspondence. Social exchange theory (Blau, 1964; Cropanzano & Mitchell, 2005) may improve our understanding of compressed work schedules (Deery et al., 2017). It proposes that employees are motivated to engage in behaviours that support organizational goals when they feel that the organization is treating them favourably. Favourable treatment can, for instance, involve the introduction of flexible or alternative work schedules (Berkery et al., 2017, 2020; Caillier, 2016; Kelliher & Anderson, 2010; Wong et al., 2020). Beyond these theoretical perspectives, expectancy theory (Vroom, 1964) can enhance our understanding of factors that promote or prevent the successful implication of alternative work schedules. In particular, it suggests that individuals’ beliefs shape their behaviour and susceptibility to positive and negative effects.

Implications for time pressure

Empirical evidence of the effect of compressed work schedules on work demands, such as time pressure, is scarce and inconclusive. Findings from Fast and Frederick (1996) suggest that time pressure increases for women but not men. Theoretical support, likewise, is inconclusive. According to work adjustment theory, the introduction of a compressed work schedule could have positive and negative implications for time pressure. While the introduction of a compressed schedule does not decrease the overall number of work hours, it might decrease the overall number of hours during which employees can work efficiently (Pencavel, 2018). Consequently, employees may experience time pressure as they cannot meet work requirements and achieve work adjustment (Dawis et al., 1968).

Conversely, employees may be able to complete more tasks on longer workdays (Chandler, 2004) because they save time setting up tasks (Arbon et al., 2012). Compressed work schedules, thus, may also enable employees to meet the requirements of their jobs more effectively (Deery et al., 2017). This might translate into reduced time pressure. As there is no clear objective to determine whether the introduction of a compressed workweek affects time pressure positively or negatively and few studies have investigated the implications of compressed work schedules for time pressure (see Fast & Frederick, 1996 for an exception), we have formulated an undirected hypothesis:

Hypothesis 1:

Time pressure changes after implementing a compressed work schedule.

Implications for fatigue and perceived productivity

The idea of the compressed workweek was introduced by employers who wanted to boost productivity (Veal, 2023) and advocates of the compressed workweek often hope that adopting a compressed workweek will amplify employee motivation (Spicer & Lyons, 2023). Social exchange theory (Blau, 1964; Cropanzano & Mitchell, 2005) supports these hopes and suggests that individuals increase their efforts in exchange for favourable practices (Kelliher & Anderson, 2010), such as flexible work arrangements (Berkery et al., 2020; Wong et al., 2020). However, research has not confirmed positive effects on motivation and productivity and empirical evidence remains inconsistent (e.g., Baltes et al., 1999; Bell et al., 2015; Cette et al., 2011; Facer & Wadsworth, 2008; Spicer & Lyons, 2023; Topp et al., 2022; Wadsworth et al., 2010; Wright et al., 2013).

Opposing theoretical perspectives suggest negative implications for productivity. Individuals may only be capable of performing optimally for a limited number of hours a day (Pencavel, 2018) before fatigue sets in (e.g., Bell et al., 2015; Ronen, 1984; Wright et al., 2013) and reduces productivity (Hyatt & Coslor, 2018). As employees on a compressed work schedule are required to work longer hours per day, albeit on fewer days, this would suggest that they work at suboptimal levels for longer periods of time than individuals working conventional work schedules. According to the theory of work adjustment (Dawis et al., 1968), this should translate to a lower congruence between job demands and individuals’ abilities and may negatively affect individuals’ perception of their performance. Thus, we hypothesized:

Hypothesis 2:

Fatigue increases after implementing a compressed work schedule.

Hypothesis 3:

Perceived productivity decreases after implementing a compressed work schedule.

Implications for work-life balance

Employees who switch to a compressed work schedule gain an extra free day and safe time commuting. This grants employees more opportunities to fulfil personal responsibilities, which contributes to a better congruence of work demands and personal needs and responsibilities, i.e., a higher work adjustment (Dawis et al., 1968; Deery et al., 2017). This likely contributes to a more satisfactory perception of the integration of personal life and work (for more thorough discussions of the concept of work-life balance see Kalliath & Brough, 2008; Sirgy & Lee, 2017). Accordingly, a growing number of studies find positive effects on work-life balance (e.g., Bilal et al., 2010; Brough & O’Driscoll, 2010; Dunham et al., 1987; Lingard et al., 2007, 2008; Saltzstein et al., 2001; Spicer & Lyons, 2023; Vega & Gilbert, 1997; Wadsworth & Facer, 2016). Hence, we hypothesized:

Hypothesis 4:

Work-life balance increases after implementing a compressed workweek.

Expectancy effects

The compressed workweek might not suit all employees, professions, and companies (Chakraborty et al., 2022; Deery et al., 2017; Wadsworth & Facer, 2016). However, the study of amendable factors that may determine how beneficial a compressed workweek is might provide valuable guidelines for practitioners when planning to adopt a compressed schedule. One such factor may be employees’ expectations.

Expectancy effects, i.e., the various ways in which expectations and beliefs influence perception, behaviour, and, subsequently, the outcomes of interventions (Vujanovic et al., 2007), can manifest in self-fulfilling manners and yield positive and negative consequences (Ostojić et al., 2016). Inaccurate negative expectations can reduce the effectiveness of interventions, while positive expectations can inflate positive outcomes (Noshari et al., 2023; Ostojić et al., 2016). Accordingly, expectancy theory (Vroom, 1964) suggests that individuals who believe that putting in effort to adapt to the compressed schedule will result in desirable outcomes may be more susceptive to benefits and motivated to “make it work.” On the contrary, those with negative expectations might hesitate to change their habits and adapt. Hyatt and Coslor (2018) provided first evidence for the effect of employee perception: They found that employees experienced greater work-life balance benefits when they were satisfied with the compressed work schedule. Consequently, we assume that employees are particularly susceptible to positive effects when they have more positive expectations and that positive expectations may buffer negative consequences of compressed work schedules.

Hypothesis 5a–d:

Expectations regarding the effect of the implementation of a compressed workweek a) affect the change in time pressure over time, b) reduce the increase in fatigue as well as c) the decrease in perceived productivity, while d) positively affecting the increase in work-life balance over time.

Materials and methods

Procedure and sample

We conducted the study among employees of a construction company that switched from a standard five-day workweek to a compressed four-day workweek (with full-time employees now working 9.75 hours per day). The company consists of white-collar workers who work primarily in an office setting and have flexible working hours and the option to work from home as well as blue-collar workers who work on-site with fixed schedules. While white-collar workers worked Monday through Thursday after the implementation, blue-collar workers worked Monday through Thursday or Tuesday through Friday. Moreover, blue-collar workers had a mandatory one-hour lunch break before the implementation, which was shortened to a 30-minute break after the implementation.

Employees were informed of the implementation of the compressed workweek two weeks in advance. During the transition to the compressed work schedule, employees were encouraged to optimize work processes and identify and reduce unnecessary tasks. The company cited two main reasons for the implementation: Firstly, they wanted to improve their employees’ well-being and work-life balance, and secondly, to increase their attractiveness to prospective employees.

Participants filled out two questionnaires. The first questionnaire was distributed to all employees (ca. 450 individuals) on the first day of the trial, and the second questionnaire three months later. When filling out the questionnaire at t1, the participants were asked to refer to the previous three months, i.e., the time before the implementation, while it was specified that they should refer to the time since the implementation at t2.

Independent researchers administered the questionnaires. Participants were informed about the procedure in advance. They were informed that data would be processed anonymously, that only the independent researchers would have access to data, and that results would only be presented to management on an organization level. Informed consent was obtained. Participation was voluntary and anonymous. Participants could fill out the questionnaires online or on paper. Paper-pencil versions were distributed by the researchers, supervisors, and the employee representative. Questionnaires were collected in closed boxes. We followed ethical guidelines from the departmental review board and the American Psychological Association (2017).

While 399 individuals returned the questionnaire at t1, 328 participants responded at t2. Using a participant-generated code, we were able to match 247 datasets. Of those, 66.8% were white-collar workers. 14.8% of the participants had leadership responsibilities. The majority of the participants were employed full-time (92.3%). While 29.1% of the participants were under 30, 49.0% were between 30 and 45, and 21.9% were older than 45. Because employees, particularly the blue-collar workers, were predominantly male, we did not collect data regarding gender to avoid compromising anonymity.

Attrition bias

To ensure that the data were representative of the staff, we compared the demographic data of the 247 participants who both participated at t1 and t2 with participants who either only participated at t1 or t2. We found no significant differences in age, leadership responsibility, tenure, and workhours. Moreover, we conferred with the organization’s Human Resources department and they confirmed that the sample was representative of the organization except for employment status. More white-collar workers (versus blue-collar workers) participated at both measurement points.

Measures

Time pressure

Time pressure was measured with four items adapted from the ISTA (Semmer et al., 1999), e.g., “In the past three months, how often were you pressed for time?” The items were answered on a Likert scale ranging from 1 = very rarely/never to 5 = very often. Cronbach’s alpha was .91 at t1 and .94 at t2.

Fatigue

Fatigue was measured with three items adapted from the German Maslach Burnout Inventory (MBI-D; Büssing & Perrar, 1992), e.g., “I feel used up at the end of the workday.” Participants indicated their agreement on a Likert scale ranging from 1 = not at all to 5 = completely. Cronbach’s alpha was .81 at t1 and .88 at t2.

Perceived productivity

Perceived productivity was measured with three items adapted from Goodman and Svyantek (1999), e.g., “In the past three months, I have fulfilled all the requirements of my job.” Participants were asked to indicate their agreement on a Likert scale ranging from 1 = not at all to 5 = completely. Cronbach’s alpha was .90 at t1 and .92 at t2.

Work-life balance

Work-life balance was measured with three items based on Syrek et al. (2011) German language short scale, e.g., “In the past three months, I was satisfied with the balance between work and personal life.” Participants indicated their agreement on a Likert scale ranging from 1 = not at all to 5 = completely. Cronbach’s alpha was .92 at t1 and .96 at t2.

Expectations regarding the effect of the implementation

To measure participants’ expectations regarding the effect of the implementation of the compressed workweek, we asked participants at t1: “Which changes are you expecting with the introduction of the four-day week in the following areas?” Participants indicated on a 5-point Kunin scale whether they expected deteriorations, no change, or improvements in the following areas: work processes, safety at work, collaboration and communication, efficiency at work, stress, job satisfaction, and work-life balance. Cronbach’s alpha was .89.

Control variables

Because prior research indicates that the impact of compressed work arrangements may vary due to sociodemographic differences (cf., Deery et al., 2017), we included age (younger than 30 years old, between 30 and 45 years old, and older than 45), employment status (blue- versus white-collar workers), and leadership responsibilities as control variables.

Data analysis

We used Latent Change Score Modelling (LCSM; McArdle, 2009) to test our hypotheses. The approach is suitable for estimating individual- and between-person changes (Ferrer & McArdle, 2010) and is used to assess how focal variables change across two or more time points (Sorjonen et al., 2022). By modelling latent change variables, which represent de- or increases between two or more measurement points, it explicitly examines change as an outcome (Klopack & Wickrama, 2020). Moreover, it allows us to analyse how predictors influence the change between measurement points (McArdle, 2009).

We used Mplus 8 (Muthén & Muthén, 2009) for data analysis. We followed procedures outlined by Klopack and Wickrama (2020) to estimate changes in time pressure, fatigue, perceived productivity, and work-life balance from t1 to t2. After specifying the measurement model by estimating latent scores for each variable at each time point, we estimated latent change scores, representing differences between latent variables at different time points. Subsequently, we added covariances to control for age, employment status, and leadership responsibilities and examined the influence of expectations regarding the effect of the implementation of a compressed workweek on the latent change scores by including expectations as a predictor of the modelled changes.

Model fit

We used the Root Mean Square Error of Approximation (RMSEA, Steiger, 1990), Comparative Fit Index (CFI), and Tucker – Lewis Index (TLI, Bentler, 1990) to estimate model fit. Acceptable model fit was found for the specified model including time pressure, strain, perceived productivity, and work-life balance (CFI = .997, TLI = .979, RMSEA = .052) and for the model including the predictor and control variables (CFI = .961, TLI = .906, RMSEA = .082).

Results

Table 1 displays the means, standard deviations, and correlations for all study variables at both time points.

Table 1. Means, standard deviations, and correlations of the main study variables at t1 and t2.

Variables			N	M	SD	1	2	3	4	5	6	7	8
1	Time pressure	t1	244	3.04	.96
2	Time pressure	t2	243	2.74	1.04	.61**
3	Fatigue	t1	238	2.92	.90	.71**	.45**
4	Fatigue	t2	241	2.65	.99	.45**	.78**	.53**
5	Perceived productivity	t1	241	4.27	.64	−.12	−.16*	−.14*	.12
6	Perceived productivity	t2	242	4.25	.73	−.01	−.18*	−.05	−.23**	.37**
7	Work-life balance	t1	240	3.45	.94	−.46**	−.27**	−.59**	−.27**	.16*	.13*
8	Work-life balance	t2	239	3.91	1.07	−.34**	−.66**	−.34**	−.72**	.14*	.35*	.22**
9	Expectations	t1	231	3.45	.74	−.19*	−.46**	−.14*	−.46**	−.01	.08	−.04	.54**

*p < .05., **p < .01.

The results of the LCSM indicate a decrease in time pressure (λ = −.39, p < .001; hypothesis 1). Contrary to hypotheses 2 and 3, we found that perceived productivity did not change over time (λ= −.02, p = .40) but that fatigue decreased after the introduction of the compressed work schedule (λ = −.31, p < .001). Confirming hypothesis 4, the results indicate that work-life balance increased over time (λ= .44, p < .001).

Next, we found that employees’ expectations negatively affected the change in time pressure (hypothesis 5a; γ = −.38, p < .001), i.e., the decrease in time pressure was stronger when expectations were higher. Likewise, we found that expectations negatively affected the decrease in fatigue (hypothesis 5b; γ = −.35, p < .001), meaning that fatigue decreased to a greater extent for individuals with more positive expectations. In contrast, expectations did not affect the change in perceived productivity (hypothesis 5c; γ = .11, p = .13). In support of hypothesis 5d, we found that expectations positively affected the increase in work-life balance (γ = .54, p < .001), meaning that work-life balance improved to a greater extent for individuals with more positive expectations. Standardized coefficients for the hypothesized relationships and covariates are presented in Table 2.

Table 2. Standardized coefficients for latent change score models.

Predictor	ΔTime pressure			ΔFatigue			ΔPerceived Productivity			ΔWork-life balance
	λ	SE	R^2	λ	SE	R^2	λ	SE	R^2	λ	SE	R^2
Expectations at t1	−.38**	.07	.18	−.35**	.08	.16	.11	.12	.04	.54**	.07	.31
Age	−.09	.06		−.15**	.06		.07	.13		.14**	.06
Employment status	.12*	.06		.09	.06		−.05	.08		−.04	.06
Leadership responsibilities	.08	.07		.07	.06		−.03	.09		−.05	.05

N = 222; R² = explained variance of latent change score. *p < .05. **p < .01.

For the control variables, we found that age significantly affected the change in fatigue (γ = −.15, p = .004) and work-life balance (γ = .14, p = .004). These results indicate that older participants experienced a greater decrease in fatigue and a greater increase in work-life balance from t1 to t2. Moreover, we found that employment status positively affected the change in time pressure (γ = .12, p = .04). Here, we found that the decrease in time pressure was greater for blue-collar workers.

Supplementary analyses

To assess at which level of expectations individuals experienced changes in the focal variables, we separated participants into three groups (low, medium, and high expectations) and conducted paired t-tests. Results are summarized in Table 3. For individuals with medium and high expectations, findings were consistent with those from the LCSM. However, for individuals with low expectations, work-life balance was lower at t2 than at t1.

Table 3. Differences in time pressure, fatigue, perceived productivity, and work-life balance between t1 and t2 for individuals with low, medium, and high expectations.

Subsample	N	Variable	t1		t2		t	df	Cohen’s d
			M	SD	M	SD
Low expectations	89	Time pressure	3.24	.93	3.31	1.01	.80	86	.09
		Fatigue	3.09	.91	3.14	.92	.58	85	.06
		Perceived Productivity	4.28	.61	4.15	.68	−1.59	86	−.17
		Work-life balance	3.51	.92	3.23	1.14	−2.10*	86	−.23
Medium expectations	64	Time pressure	3.04	.95	2.62	.93	−4.02**	68	−.48
		Fatigue	2.86	.82	2.54	.89	−3.10**	68	−.37
		Perceived Productivity	4.20	.55	4.31	.59	1.22	68	.15
		Work-life balance	3.31	.95	4.15	.80	6.89**	67	.84
High expectations	73	Time pressure	2.86	.97	2.25	.87	−6.50**	72	−.76
		Fatigue	2.84	.94	2.26	.91	−4.92	69	−.59
		Perceived Productivity	4.33	.76	4.31	.91	−.24	72	−.03
		Work-life balance	3.39	.93	4.49	.73	8.87	69	1.06

Percentile values of employees’ expectations were used to separate the sample into three groups (33.33%ile = 3.14; 66.66%ile = 3.71); *p < .05, **p < .01.

In contrast to prior findings (Arbon et al., 2012; Dunham & Hawk, 1977; Saltzstein et al., 2001), our results suggest that older individuals benefited more from the introduction of the compressed workweek. To better understand the role of age, we decided to conduct paired t-tests for different age groups. Results are presented in Table 4. Importantly, differences in time pressure, fatigue, and work-life balance were most pronounced for individuals between 30 and 45.

Table 4. Differences in time pressure, fatigue, perceived productivity, and work-life balance between t1 and t2, depending on age.

Subsample	N	Variable	t1		t2		t	df	Cohen’s d
			M	SD	M	SD
Younger than 30	72	Time pressure	2.87	.92	2.78	1.13	−.75	71	−.09
		Fatigue	2.90	.88	2.85	1.05	−.50	70	−.06
		Perceived Productivity	4.23	.79	4.18	.84	−.56	69	−.07
		Work-life balance	3.33	.92	3.75	1.14	2.68**	70	.32
30 to 45 years old	118	Time pressure	3.23	.95	2.83	1.04	−4.98**	117	−.46
		Fatigue	3.05	.93	2.68	.98	−4.29**	115	−.40
		Perceived Productivity	4.24	.59	4.18	.74	−.67	116	−.06
		Work-life balance	3.39	.99	3.86	1.11	3.79**	115	.35
Older than 45	53	Time pressure	2.87	.97	2.50	.86	−3.29**	51	−.46
		Fatigue	2.76	.82	2.31	.85	−3.09**	50	−.43
		Perceived Productivity	4.41	.52	4.51	.47	1.53	52	.21
		Work-life balance	3.75	.78	4.24	.74	3.59**	49	.51

*p < .05., **p < .01.

Discussion

Research, including the present study, confirms the general assumption that compressed work schedules contribute to an improved alignment between work demands and private needs and responsibilities, particularly when it entails a long three-day weekend (Brown et al., 2011; Kenny, 1974). Researchers previously claimed that not compressed work schedules but temporal flexibility may be more central for work-life balance (Higgins et al., 2014, Tausing & Fenwick, 2001; Wadsworth & Facer, 2016; Wadsworth et al., 2010). We, however, found that employees benefited from the compression of their work hours, even though they did not gain more flexibility regarding their work schedules. As gaining control over one’s spare time, too, may play an important role, elements of the demand-control model (Karasek, 1979), nevertheless, may complement work adjustment theory (Dawis et al., 1968) and contribute to a more nuanced understanding of compressed work schedules by integrating effects not only on aspects of work but of private life.

We found decreases in time pressure after a compressed work schedule was implemented. Longer workdays may allow employees to complete more tasks when they can work continuously on them (Chandler, 2004), decreasing time pressure and fostering higher work adjustment. As work adjustment theory (Dawis et al., 1968) can be applied to predict either positive or negative consequences for time pressure, our findings suggest that specific characteristics of workplaces and processes need to be considered. Working longer hours per day may be particularly beneficial for employees who need to invest much time in preparing work tasks or who need long periods of time to complete tasks. In contrast, a compressed work schedule may lead to poorer work adjustment for employees who only need little time to set up tasks or work spaces and whose jobs require intense concentration. Accordingly, we found that blue-collar workers, whose work consists of tasks that require more preparation, such as preparing and cleaning away tools, benefited more regarding time pressure than white-collar workers. In the present organization, moreover, the introduction of the compressed work schedule was accompanied by changes in work processes. In particular, employees were encouraged to optimize processes and reduce unnecessary tasks, such as lengthy meetings. These changes likely contributed to the reduction of time pressure.

For fatigue, both theory and research suggest negative implications of long work hours (e.g., Bannai & Tamakoshi, 2014) and the compressed workweek (e.g., Bell et al., 2015; Ronen, 1984; Wright et al., 2013). However, we found that fatigue decreased after the introduction of the compressed workweek. The actual number of hours that employees work may be an essential factor in understanding employee fatigue. Amendola et al. (2011), for instance, note that 12-hour shifts may have negative implications for fatigue but that 10-hour shifts do not. Similarly, the number of overtime hours before and after the introduction of a compressed work schedule may determine how the switch to a compressed schedule affects fatigue. While employees are contractually required to work longer hours per day after adopting a compressed schedule, actual work hours may not differ when overtime decreases with the implementation.

Moreover, short-, medium-, and long-term effects of compressed work schedules may differ. Working on a compressed four-day schedule may entail adverse short-term effects as it increases the amount of effort that needs to be spent during the workday, while it simultaneously reduces the time available for personal responsibilities, leisure, and recovery during the workweek (Van der Hulst & Geurts, 2001). This likely increases fatigue at the end of workdays (Goodale & Aagaard, 1975). However, recovery research implies that longer periods during which no effort needs to be exerted offer greater opportunities for recovery (Sonnentag & Fritz, 2007). Thus, compressed work schedules encompass the opportunity for medium- or long-term improvements because the extra day off may entail greater recovery opportunities. This may alleviate fatigue long-term. The effort-recovery model (Meijman & Muldner, 1998) may provide a useful framework to further study the short- and medium- to long-term effects on fatigue.

Distinguishing between short-, medium-, and long-term effects when determining the effects of compressed work schedules may also allow to integrate contrasting theoretical assumptions regarding (perceived) productivity. While we found no changes in perceived productivity three months after the implementation of a compressed schedule, which aligns with some previous research (Topp et al., 2022; see Baltes et al., 1999 for an early meta-analysis), reciprocity effects that encourage increased effort among employees in return for favourable treatment by the organization (cf., social exchange theory, Blau, 1964; Cropanzano & Mitchell, 2005) may result in a short-term productivity boost. These, however, may fade over time (Ivancevich & Lyon, 1977) and reveal poor adjustment of work demands and personal abilities (cf., theory of work adjustment, Dawis et al., 1968).

Overall, the effect of implementing a compressed work schedule seems contingent on contextual factors, such as employees’ expectations. Individuals with medium and high expectations regarding the introduction of the compressed workweek were able to benefit, while those with low expectations did not benefit from the alternative schedule at all. These findings align with prior research, which indicates that expectations can affect the effectiveness of interventions (e.g., Mothes et al., 2017; Noshari et al., 2023; Tambling, 2012). In self-fulfilling manners (Ostojić et al., 2016), employees who have more negative expectations may be less engaged in the change process (Snippe et al., 2015) and less inclined to invest efforts in ensuring the success of the new work schedule, while those with high hopes, may be more resolute to attain optimal functioning. This highlights the need to incorporate individuals’ perceptions into theoretical frameworks to increase our understanding of the effect of compressed work schedules. In particular, focusing only on work adjustment theory (Dawis et al., 1969) may be too simplistic and neglect the role of expectancy (cf., expectancy theory, Vroom, 1964) and reciprocity effects (cf., social exchange theory, Blau, 1964; Cropanzano & Mitchell, 2005).

Our findings, and previous research, suggest that the compressed workweek is not equally suitable for all employees (Deery et al., 2017). The compressed workweek is, for instance, understood to be less beneficial for older employees, while young people stand to gain more (Dunham & Hawk, 1977; Saltzstein et al., 2001). We, however, found that individuals between the ages of 30 and 45 benefitted most from the introduction of the compressed workweek, particularly regarding fatigue and work-life balance. Employees between the ages of 30 and 45 are likely most challenged to align work and personal demands as they are the group most likely caring for young children. Gaining a free day may be used to fulfil these responsibilities. This, however, may only apply to individuals with partners who can care for their children on long workdays. As women remain primarily responsible for care work (Moreira da Silva, 2019), these effects might not replicate in a predominantly female sample. For fatigue, we observed that younger individuals reported lower levels than in the other age groups before the introduction of the compressed work schedule, which might explain why they did not experience a reduction in fatigue after the introduction of the compressed workweek. This is in line with Arbon et al. (2012), who claim that young employees may be more able to sustain the exertion of energy for longer periods of time.

Practical implications

Substantial relevance of the present study is rooted in its provision of guidelines for practitioners interested in adopting alternative work schedules. In particular, the present paper highlights that the compressed workweek may not be equally suitable for all employees. Concerns regarding trade-offs cannot be dismissed and employees’ needs and wishes need to be carefully deliberated before implementing a compressed work schedule.

Importantly, our findings highlight a factor critical for success: Employees’ expectations. Organizations should, therefore, carefully assess and manage their employees’ anticipations before introducing alternative work schedules, such as the compressed workweek. Change can cause anxiety because it increases uncertainty (Topping, 2002). However, organizations can take several measures to increase the acceptance of change. They can acknowledge uncertainty and employees’ feelings about the impending change (Gagné et al., 2000). Listening to employees’ concerns and providing guidelines and feedback may help employees maintain well-being when adapting to a new schedule (DiFabio & Gori, 2016; Gigliotti et al., 2019). Moreover, it is advisable to grant autonomy to employees. This can be achieved by explaining the underlying reasons for the change and providing options for input and choice (Gagné et al., 2000). In addition, leadership is important in fostering acceptance of change (Voet, 2016).

Limitations and future directions for research

The present study yields valuable insights regarding the effects of compressed work schedules and adds longitudinal evidence for the effects on time pressure, fatigue, perceived productivity, and work-life balance. Moreover, we showed that employees’ expectations influence the effect of the implementation of a compressed workweek. However, some methodological limitations need to be considered.

Firstly, there was no control group. Consequently, we cannot rule out time-effects or other underlying reasons for the observed changes. For instance, employees were encouraged to utilize the change in the work schedule to optimize work processes. It can, thus, not be determined whether the observed changes are, at least in part, the result of the employees’ efforts to enhance operational efficiency.

Secondly, our findings are based on a predominantly male sample from only one organization, which limits generalizability. Compressed work schedules may create conflicts between work and private demands for people with care responsibilities. This might affect women more than men because women are still primarily responsible for care work (Moreira da Silva, 2019). We were, however, unable to assess the influence of gender because we did not collect information regarding gender due to anonymity concerns. Likewise, our sample is limited to one construction company. Effects of compressed work schedules, however, may vary across organizations, professions, and industries.

Thirdly, we only assessed medium-term changes in the focal variables. Campbell (2023), however, points out that more evidence for long-term effects is needed. This is particularly relevant because it remains uncertain whether effects become stronger (e.g., Nord & Costigan, 1973) or fade with time (e.g., Ivancevich & Lyon, 1977). Additionally, the study of daily effects would add valuable insights as there may be short-term tolls of long work hours, such as increased fatigue (Goodale & Aagaard, 1975) and reduced satisfaction with the compatibility of work and personal responsibilities during the workweek.

Conclusion

The findings of this study provide valuable insights into the effects of compressed work schedules. The observed improvements in work-life balance and the alleviation of fatigue and time pressure suggest that adopting such alternative work schedules can have positive implications for employees without impacting perceived productivity. Importantly, however, positive effects were contingent on employees’ expectations. While the present findings may increase clarity for researchers and practitioners interested in implementing alternative work schedules, the present findings must be understood in light of previous research, suggesting inconsistent effects on various indicators of employee well-being and organizational functioning. Further research with robust methodologies is needed to differentiate between short-, medium- and long-term effects and further clarify under which circumstances and for whom compressed work schedules can be beneficial.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this study are available from the corresponding author, A. M., upon reasonable request.

Additional information

Funding

Institutional resources covered expenses related to data collection, analysis, and project-related activities.