Work engagement has been conceptualized as a relatively stable individual difference variable, but research has shown that there are within-person variations in the experience of work-engagement (Macey & Schneider, 2008; Sonnentag, 2003; Xanthopoulou & Bakker, 2012). Moreover, experience sampling studies and diary studies have shown extensive within-person variations in work-related affective experiences, as well as fluctuations from one day to another (Sonnentag, 2003). This implies that work engagement not only differs between employees, but also within-person and from one day to another (Bakker & Bal, 2010). Therefore, scientific research has defined daily work engagement as a short-term, fulfilling, positive and work-related state of mind that is characterized by dedication, vigor and absorption and fluctuates within individuals over a short period of time (Breevaart, Bakker, &
Demerouti, 2014). Thus, the definition of daily work engagement is rather similar to the general definition of work engagement. However, there are two differences. Firstly, daily work engagement refers to a short-term state of mind, and secondly, this state of mind happens over a short period of time, namely one day (Breevaart et al., 2014; Schaufeli et al., 2002).
So far, we have discussed what work engaged employees are and why they are more likely to perform better at their job. Although work engaged employees experience feelings of vigor, dedication and absorption at their work, this may be confused with being too attached to one’s work. When an
employee is too attached to his work he could be a workaholic (Bakker, Schaufeli, Leiter, & Taris, 2008).
However, in contrast to workaholism, positive consequences of highly engaged employees include a better work performance, more organizational commitment and better health (Bakker, Hakanen, Demerouti, & Xanthopoulou, 2007; Schaufeli & Taris, 2013). In order to maintain or increase employee work engagement, several factors seem to be important, for example, having sufficient job resources to cope with job demands (Bakker & Demerouti, 2007; Demerouti, Nachreiner, Bakker, & Schaufeli, 2001;
Schaufeli & Bakker, 2004).
Job resources, job demands and work engagement
Employees have a bigger chance of becoming more engaged in their work when they have sufficient job resources (Bakker & Demerouti, 2007). Moreover, daily work engagement may be enhanced by a motivational process that is stimulated through the presence of job resources (Breevaart et al., 2014).
Job resources refer to those physical, organizational and social aspects of the job that could reduce the job demands, help in the achievement of work goals and stimulate the employees personal growth, learning and development (Bakker & Demerouti, 2007; Schaufeli & Bakker, 2004). This means that when an employee has sufficient resources, the employee should experience support, for example, through performance feedback, autonomy, social support, skill variety and learning opportunities (Bakker &
Demerouti, 2007).
The importance of resources could be further explained by making use of the conservation of resources (COR) theory (Schaufeli & Bakker, 2013). The COR theory can be seen as a motivation theory where the main principle states that employees wish to gain resources, hold on to their existing resources or regain lost resources (Hobfoll, 2001; Schaufeli & Bakker, 2013). When an employee gains resources, it becomes easier to perform the work duties, which would make the employee more successful and encourage more positive feelings towards the job (Reis, Hoppe, & Schröder, 2015). This increase in resources could spark a repeating effect (i.e. it is easier to gain more resources, when an employee already has
resources) when the resources are accumulated, turning the resource gain into a cycle; which is conveniently called a gain cycle (Schaufeli & Bakker, 2013). Therefore, when an employee is more engaged, it is more likely that the accumulation of resources already in his possession will grow
(Schaufeli & Bakker, 2013). Finally, in line with ideas from the COR theory, the increase in job resources will lead to more work engagement (Schaufeli, Bakker, & van Rhenen, 2009).
However, when job resources are limited or job demands are too high, a situation could arise where job stress (created by this situation) may become a problem (Bakker et al., 2005). Job demands apply to those physical, organizational or social aspects of work that need sustained physical or mental effort and are therefore related to physiological and psychological costs (Bakker, Demerouti, & Euwema, 2005;
Demerouti & Bakker, 2011). When these demands become too much for an employee to cope with and the demands require too much effort they may turn into job stressors, which could in turn lead to symptoms of anxiety, depression, or even burnout (Schaufeli & Bakker, 2004). Job resources play an important role when trying to improve work engagement when there are a lot of job demands present (Schaufeli & Taris, 2013). This could also be interpreted as that job resources moderate the effects of job demands (Schaufeli & Taris, 2013). So for employees to decrease the risk of developing symptoms of burnout and to be able to create a situation where the employee feels work engaged, it is important to have job resources available (Bakker et al., 2005; Schaufeli & Taris, 2013).
Despite the possible negative consequences of job demands on employees, one may ask whether there are also positive outcomes of job demands. A meta-analysis study implicates that the relationship between job demands and work engagement relies upon the perception of the demand by the employee (Crawford, LePine, & Rich, 2010). If the perception of the employee is that he or she interprets the job demand as a challenge, the job demand is positively related to work engagement since the job demand may act as an intrinsic motivator (Sawang, 2012). An example of a challenging job demand could be intellectual demand, where an employee is given a difficult project but has the chance to develop professional skills at the same time. Another example of a job demand could be when an employee is given great responsibility, which was sought after by the employee. Receiving such great responsibility should then have a motivating effect on the employee, which may then result in work engagement.
However, when the employee perceives the job demand as an obstruction, the job demand is negatively related to work engagement (Sawang, 2012). Furthermore, it is more likely that an employee will be less stimulated and engaged with their work, when job demands are low (e.g. assembly line work, routine jobs) (Sawang, 2012). When job demands increase up to a point where the demands become more challenging but not too high, it is more likely that the employee will become more engaged in his work.
Nevertheless, when the job demands become too much (overload), this engagement could decrease, and turn into exhaustion (Sawang, 2012). Thus, it may be stated that some job demands can have positive consequences, but that an overload of demands may lead to exhaustion.
The relationship between job resources, job demands, and work engagement may be further explained using the Job Demands and Resources (JD-R) model. The JD-R model combines the effects of both job demands and resources, and can be seen as an overall foundation for promoting work engaged employees (Demerouti et al., 2001). The JD-R model suggests that burnout might be provoked by multiple aspects related to the work environment (Bakker et al., 2003). According to the JD-R model, becoming vulnerable to long-term job demands and lack of job resources are prognostic for burnout (Hakanen & Schaufeli, 2012). Job demands seem to be positively related to exhaustion, whereas job resources seem to be negatively related to cynicism and lack of professional efficacy, which are all factors of burnout (Bianchi, Schonfeld, & Laurent, 2015; Llorens, Bakker, Schaufeli, & Salanova, 2006).
The effects of job resources and job demands can induce two psychological processes (Llorens et al., 2006). Firstly, the job demands process could be described as the health impairment process, where the long-term job demands could lead to a deterioration of health (Hakanen, Bakker, & Schaufeli, 2006;
Schaufeli & Bakker, 2004) and sick leave (Bakker, Demerouti, de Boer, & Schaufeli, 2003). Secondly, the job resources process could be described as the motivational process (Llorens et al., 2006). The
motivational process stimulates employee’s motivation, because of the availability of more job resources (Hackman & Oldham, 1980), which in turn stimulates an increase in work engagement and more
progressive work outcomes, such as organizational commitment and employee performance (Salanova, Agut, & Peiró, 2005). According to the JD-R model, job demands can moderate the effect of job
resources on work engagement, whereas job resources can moderate the effect of job demands on burnout. Furthermore, the JD-R model illustrates how job resources could reduce the negative effects
that job demands might have on the development of burnout. Hence, the risk of burnout increases, and work engagement decreases when job demands are high and job resources are low. In the JD-R model, not only the negative psychological state is described (i.e. burnout), but also a positive corresponding state (i.e. work engagement) (Bauer, Hämmig, Schaufeli, & Taris, 2014). Thus, the model illustrates that when an employee experiences sufficient job resources to meet the job demands, this is related to work engagement through the motivational process (Van Den Broeck, Vansteenkiste, De Witte, & Lens, 2008).
Job autonomy and work engagement
As described in the JD-R model, having sufficient job resources is essential for reaching a state of work engagement. Job autonomy is a job resource that describes the degree to which a job gives freedom to an employee, the independence and discretion to the individual in scheduling work, as well as the freedom to determine the procedures for carrying out job assignments (Hackman & Oldham, 1975).
Research has found evidence for the positive and significant relationship between job autonomy and work engagement (Bakker & Demerouti, 2007; Demerouti et al., 2001; Hakanen et al., 2006; Schaufeli &
Bakker, 2004; Vera, Martínez, Lorente, & Chambel, 2016). Furthermore, there are also models and theories which support the positive effects and significance of job autonomy. For instance, the demand-control model (DCM) states that when an employee has job autonomy, job stress is reduced, even under the pressure of work overload or excessive job demands (Karasek, 1979, 1998). Moreover, according to the effort and recovery theory (Meijman & Mulder, 1998), job resources (i.e. job autonomy) and employees’ effort to perform a job well, are more likely to improve the working conditions (Schaufeli &
Taris, 2013). Therefore, job autonomy could enlarge the employees’ intrinsic motivation and as a result could boost work engagement, learning at work and organizational responsibility (Taipale, Selander, Anttila, & Nätti, 2011). From the known job resources, it has been expressed that job autonomy is an example of a job resource that bolsters work engagement (Schaufeli & Salanova, 2007). Moreover, a sense of control and autonomy during work are more likely to decrease emotional exhaustion, reduced personal accomplishment and cynicism, which are the dimensions of burnout (Alarcon, 2011). All in all, the aforementioned scientific research suggests that the availability of job autonomy may reduce the effects of the dimensions that predict burnout. Furthermore, job autonomy may stimulate the motivational process of the employee mentioned in the JD-R model, and thus also stimulate work engagement (Alarcon, 2011; Van Den Broeck et al., 2008). Thus, an increase in the job resource job autonomy may enable the employee to better cope with job demands. Therefore, job autonomy may also moderate the relationship between job demands and work engagement (Meijman & Mulder, 1998).
Sleep quality and work engagement
As mentioned previously, having sufficient resources available is important for achieving a state of work engagement. Therefore, being able to replenish resources may play a substantial role. As mentioned in the effort and recovery theory, recovery is important for the replenishment of job resources (Meijman &
Mulder, 1998). Thus, recovery, which is in part achieved through sleep, may play a vital role in stimulating work engagement (Ebert et al., 2015; Meijman & Mulder, 1998; Schaufeli et al., 2009). A large number of scientific research has focused on the importance of sleep.
As a result of this research, sleep has often been referred to as the most basic and important factor in the recovery process (Ebert et al., 2015). Moreover, for employee recovery, higher levels of sleep quality may play an important role, since sleep quality is a relevant factor for employee well-being (Hahn et al., 2011). The term ‘’sleep quality’’ is an important and complex construct, that has not yet been defined in the literature with an established definition (Crivello, Barsocchi, Girolami, & Palumbo, 2019). An example of a measure that is often used to capture sleep quality, is the Pittsburgh Sleep Quality Index (PSQI). The PSQI is used to portray a global view of sleep quality through the use of subjective, self-reported diaries
or questionnaires (Crivello et al., 2019; Krystal & Edinger, 2008). For the purpose of this research, it was assumed that sleep quality includes not only the more easily quantifiable components of sleep, such as sleep latency, sleep efficiency and total hours of sleep, but that sleep quality also includes subjective indices such as self-perceived quality of sleep and self-perceived recovery. Thus, sleep quality might be defined as a subjective measure of sleep, that tries to capture not only the quantifiable components of sleep, but also a person’s subjective experience of sleep (Buysse, Reynolds, Monk, Berman & Kupfer, 1989; Crivello et al., 2019; Krystal & Edinger, 2008; Pilcher, Ginter, & Sadowsky, 1997). In this study, several aspects of sleep have been used to measure sleep quality. The quantifiable components of sleep included in this study are total sleep, sleep latency and sleep efficiency. Sleep latency is defined as the time between bed and the onset of sleep (Van Der Schuur, Baumgartner, & Sumter, 2019). Moreover, total sleep describes the total hours slept during the night. Lastly, sleep efficiency can be defined as the ratio between the total sleep time and the time spent attempting to initially fall asleep and sleep discontinuity (Reed & Sacco, 2016). These objective measures of sleep quality could be accompanied by two subjective measures of sleep, for instance, self-perceived sleep quality and self-perceived recovery.
The inclusion of both subjective and objective measures to assess sleep quality could give more insight in the employees’ recovery, which might affect an employees’ work engagement during the next day (Sonnentag, 2003).
Although few studies have researched the relationship between sleep quality and work engagement, the effort-recovery model could offer a hopeful theoretical explanation for the relationship between sleep quality and work engagement (Barber, Grawitch, & Munz, 2013). According to this model, employees need to recover from the job demands from one day in order to be able to perform well on the next day (Meijman & Mulder, 1998). When employees go to sleep, their resource levels have the opportunity to reenergize so that they may be used by the employee during the next day (Baumeister, Muraven, & Tice, 2000). Following the doctrine of the COR theory, restoration of resources enables the employee to gain more resources (Schaufeli et al., 2009). Subsequently, as also illustrated by the JD-R model, an employee with more resources is then able to experience higher levels of work engagement (Salanova et al., 2005;
Schaufeli et al., 2009). Thus, the revival of an employees’ resources while sleeping may enable the employee to experience more work engagement on the following day (Kühnel, Zacher, de Bloom, &
Bledow, 2017; Salanova et al., 2005; Schaufeli et al., 2009; Sonnentag, 2003). Could it therefore be argued that higher sleep quality leads to a decreased effect of job demands on work engagement, meaning that employees are then better able to cope with job demands?
As mentioned previously, job resources moderate the effects of job demands (Schaufeli & Taris, 2013).
Furthermore, the restoration of resources during sleep provides the employee with the necessary fuel to handle the job demands at work (Bakker et al., 2014; Breevaart & Bakker, 2018). Because energy is needed to restore the resources that are used to cope with job demands, it suggests that for employees to become more engaged, they need a good recovery (Sheng, Wang, Hong, Zhu, & Zhang, 2019). Hence, since an employee needs resources to buffer the effects of job demands, this means that higher sleep quality could positively affect the level of an employee’s daily work engagement and thus buffer the negative effects of job demands (Kühnel et al., 2017; Salanova et al., 2005; Schaufeli et al., 2009;
Schaufeli & Taris, 2013; Sonnentag, 2003). Therefore, the relationship between job demands and daily work engagement may be moderated by sleep quality (Kühnel et al., 2017; Salanova et al., 2005;
Schaufeli et al., 2009; Schaufeli & Taris, 2013; Sonnentag, 2003).
Research model
In this research, momentary work engagement, momentary job demands and momentary autonomy of an employee were measured for one individual at several moments during the day. So far, only research on work engagement on a daily level or person level has been done. To the best of my knowledge, no other research has been done that had previously investigated these relations at a momentary level, and this research may thus be considered a gap in the literature.
To summarize, according to the JD-R model, it was expected in this research that momentary job demands negatively affect momentary work engagement (H1). Furthermore, higher daily sleep quality may lead to the replenishment and enhancement of the effects of job resources needed by an employee for coping with momentary job demands and, as a consequence, for achieving a state of momentary work engagement. Therefore, high daily sleep quality, which is measured through self-perceived sleep quality, self-perceived recovery, observed sleep latency, observed sleep efficiency and observed total sleep time, may buffer the effect of momentary job demands on momentary work engagement, and low daily sleep quality may enhance the effects of momentary job demands on momentary work
engagement (H2). Additionally, more momentary job autonomy was expected lead to more momentary work engagement, as it should positively affect an employees’ momentary work engagement directly through the presence of the job resource momentary job autonomy (H3). Furthermore, momentary job autonomy was expected to enhance an employees’ ability to cope with momentary job demands, thus buffering the effect of momentary job demands on momentary work engagement (H4). Moreover, better daily sleep quality may improve momentary work engagement and replenish job resources (i.e.
job autonomy), so sleep quality might enhance the effect between momentary job autonomy and momentary work engagement. Therefore, high daily sleep quality may enhance the effects of
momentary job autonomy on momentary work engagement and low daily sleep quality may hamper the effects that momentary job autonomy has on momentary work engagement (H5). Figure 1 illustrates research model and the possible relations between momentary job demands, momentary job autonomy, daily sleep quality and momentary work engagement.
Figure 1: Research Model
Research design
This section discusses the research design, participants, procedure, measures and analyses used in this research. To investigate work-related experiences throughout the day, a field study using the Experience Sampling Method (ESM) was initiated (Oerlemans & Bakker, 2013). To track subjective sleep in terms of timing, duration and quality, daily diaries were used. Light exposure, sleep timing and sleep duration were monitored objectively using wearable sensors that were worn during working days and on the weekend. ESM was used to collect data on job demands, job autonomy and work engagement in seven instances during the day and when the participant was awake, and he would maintain a sleep diary once a day when waking up. Per subject, this resulted in a total of seven (1 x 7) observations pertaining to sleep, and a maximum of 49 (7 x 7) observations for each of the ESM variables. The demographic information was collected before the start of the experiment.
Participants
This thesis was part of a larger study conducted by a PhD candidate, that researched the short-term effects of light exposure on burnout-related symptoms. Convenience sampling was used to select participants by asking students attending a master’s course in Performance Enhancement at the Eindhoven University of Technology to recruit one participant each, amounting up to 33 participants.
Furthermore, a group of four students from a research project recruited eight participants and a graduating masters student recruited ten participants. Participation in this study was voluntary and confidential. Participants were required to be 30 years or older and employed for at least four days a week. The total number of participants was 51, of which 33 were men and 18 women. The average age of the participants was 49.33 years (SD = 9.17), ranging from 30 to 65 years. Most participants (N = 40) had completed higher education, while the remaining participants (N = 11) had finished lower education.
The sectors in which participants worked varied from construction to transport, education, government,
The sectors in which participants worked varied from construction to transport, education, government,