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In this paper, we examined the within-person relations between morning recovery level (i.e., feeling refreshed and replenished) and work engagement throughout the day, and between work engagement throughout the day and the subsequent recovery level at the end of the workday. We hypothesized that job stressors (situational constraints, job demands)...
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... 0.747; RMSEA ϭ 0.093, S-B 2 ϭ 446.2800; df ϭ 7; p Ͻ .001); the best fitting two-factor model ( 2 ϭ 1556.545; df ϭ 298; CFI ϭ 0.399; RMSEA ϭ 0.142, S-B 2 ϭ 621.0137; df ϭ 9; p Ͻ .001); and a one-factor model ( 2 ϭ 2266.581; df ϭ 299; CFI ϭ 0.399; RMSEA ϭ 0.142); S-B 2 ϭ 1,288.64; df ϭ 10; p Ͻ .001). Thus, the five end-of-workday variables clearly represent distinct constructs. Moreover, to establish that the recovery items (including the item “I am full of new energy”) did not incidentally measure work engagement, we conducted an exploratory factor analysis of recovery and engagement items using the two-level option in Mplus. This analysis (conducted separately for recovery data assessed in the morning and recovery data assessed at the end of the workday) showed a two-factor solution in which the four recovery items had an average loading of .83 (morning) and .80 (end of workday) onto the recovery factor, and a maximum cross-loading onto the work engagement factor of .12 (morning) and .15 (end of workday). Person-level measures. At the person level, we assessed a person’s general level of recovery and general level of work engagement. For assessing the general level of recovery, we used the four-item scale developed by Sonnentag and Kruel (2006; sample item: “During my free time, I feel mentally recovered”). Cronbach’s alpha was .86. We measured general level of work engagement with the nine-item version of the UWES (Schaufeli et al., 2006). Cronbach’s alpha was .91. We included these variables as control variables in order to take person-level differences into account when predicting day-specific work engagement and the day-specific recovery level at the end of the workday. Because work engagement and level of recovery may depend on job control experienced (Bakker & Demerouti, 2007; van Veldhoven & Sluiter, 2009), we also controlled for job control. We used five items from the scale developed by (Semmer, 1984; Zapf, 1993), to be answered on a 5-point Likert scale (1 ϭ very little ; 5 ϭ to a high degree ). A sample item is “Can you influence the way in which you accomplish your tasks?” Cronbach’s alpha was .78. Our data set comprised data at the person level (e.g., person- level control variables) and at the day level (e.g., day-specific work engagement), with day-level data nested within persons. To take the noninterdependence of these data into account, we used hierarchical linear modeling to analyze the data. Before testing our hypotheses, we examined within- and between-person variation in our two outcome variables (work engagement and recovery level at the end of the workday). As can be seen in the null model for work engagement (see Table 2), within-person variance (Level 1 variance) was 0.164 and between- person variance (Level 2 variance) was 0.345, resulting in a total variance of 0.509. Thus, for work engagement, within-person variance was 32.2% and between-person variance was 67.8%. For recovery level at the end of the workday (see Table 3), within- person variance was 0.383 and between-person variance was 0.360, corresponding to 51.5% and 48.5%, respectively. To test our hypotheses, we compared several nested models. The null model included only the intercept. In Model 1, we included control variables; in Model 2, we entered the main effects; and in Model 3, we included the interaction effects. We tested the improvement of each model over the previous one by using the difference between the respective likelihoods. This difference follows a chi-square distribution, with the degrees of freedom corresponding to the number of parameters added to the model. Hypotheses 1 to 3 proposed that morning recovery level will predict work engagement and that day-specific job demands and day-specific situational constraints will moderate the relation between morning recovery level and work engagement. Table 2 shows the results from hierarchical linear models predicting day- specific work engagement from morning recovery level, job stressors (job demands, situational constraints), and the interaction terms between recovery level and job stressors (job demands, situational constraints). Model 1—including job control and general level of work engagement as control variables—showed a better model fit than the null model. A person’s general level of work engagement was a highly significant predictor of day- specific work engagement. Model 2—including the main effects— showed a further improvement over Model 1, with all three predictor variables being significant. Morning recovery level and job demands were positively related and situational constraints were negatively related to work engagement. Model 3—including the interaction terms—fit the data significantly better than Model 2. The interaction term between morning recovery level and situational constraints was significant. 2 To gain more insight into the pattern of this interaction, we performed simple slope tests (Preacher, Curran, & Bauer, 2006). As Figure 2 illustrates, on days with high levels of situational constraints (one SD above the mean), morning recovery level was not related to work engagement; ( ␥ ϭ Ϫ 0.062; SE ϭ 0.079; z ϭ Ϫ 0.782; ns ), but on days with low levels of situational constraints (one SD below the mean), morning recovery level was positively related to work engagement ( ␥ ϭ 0.184; SE ϭ 0.066; z ϭ 2.811; p Ͻ .01). The interaction term between morning recovery and job demands, however, was not significant. Taken together, our data provided support for Hypotheses 1 and 3 but not for Hypothesis 2. Hypotheses 4 and 5 referred to the prediction of recovery level at the end of the workday. We hypothesized that work engagement during the day will be positively related to the recovery level at the end of the workday (Hypothesis 4) and that situational constraints will moderate the relation between work engagement and recovery level at the end of the workday (Hypothesis 5). In these analyses, we controlled for job control, general level of recovery, morning recovery level, and negative affect at the end of the workday because all these variables may have an impact on recovery level at the end of the workday. When testing the interaction effect between work engagement and situational constraints, we pursued a conservative strategy and also included the interaction effect between work engagement and job demands. Table 3 shows the results. Model 1—including the control variables—showed a better model fit than the null model. Job control and general level of recovery were positively related to recovery level at the end of the workday. Negative affect at the end of the workday showed a strong negative association with recovery level at the end of the workday. When we entered work engagement, job demands, and situational constraints into Model 2, model fit further improved. Work engagement was positively related to recovery level at the end of the workday, providing support for Hypothesis 4. Neither job demands nor situational constraints were significantly related to recovery level at the end of the workday. Model 3—including the interaction effects— had a better model fit than Model 2. The estimate of the interaction between work engagement and situational constraints was significant. 4 Simple slope tests (Preacher et al., 2006) showed that on days when situational constraints were low (one SD below the mean), work engagement during the workday had a strong positive association with the recovery level at the end of the workday ( ␥ ϭ 0.542; SE ϭ 0.125; z ϭ 4.335; p Ͻ .001). On days with a high level of situational constraints (one SD above the mean), work engagement was not related to the recovery level at the end of the workday ( ␥ ϭ 0.140; SE ϭ 0.142; z ϭ 0.992, ns ; cf. Figure 3). Overall, these findings support Hypothesis 5. Our study showed that morning recovery level predicted work engagement during the workday and that work engagement, in turn, predicted recovery level at the end of the workday. These reciprocal relations between recovery level and work engagement did not occur under all circumstances. Situational constraints attenuated the association between morning recovery level and work engagement during the day and between work engagement during the day and subsequent recovery level. Although our data cannot demonstrate causality in a strict sense, the pattern of findings might imply that recovery level and work engagement mutually reinforce each other: The more recovered an employee is in the morning, the more engagement the employee will experience at work, which limits the decrease in the employee’s recovery level over the course of the day. Situational constraints interrupt these reciprocal processes between recovery level and work engagement. Our findings, focusing on within-person fluctuations of recovery level and work engagement, extend results from studies conducted at the between-person level. These studies have identified reciprocal associations between resources, such as optimism and pride in one’s profession on the one hand and work engagement on the other hand (Hakanen, Perhoniemi, & Toppinen-Tammer, 2008; Xanthopoulou et al., 2009a). Thus, when recovery level is conceptualized as a resource (Binnewies et al., 2009), our findings point in a similar direction: Resources, including recovery level, facilitate work engagement, which in turn helps to keep resources at a higher level than when work engagement is low. We have to note that in absolute terms, recovery level goes down during the course of the workday. However, one might speculate that over a series of days with high levels of morning recovery and high levels of work engagement during the day, gain cycles might occur that are reflected in increasing recovery levels and increasing levels of work engagement over the course of several days. We identified situational constraints as harmful moderators in the recovery– engagement process. On days when situational ...
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Citations
... Whereas previous generations of workers of detachment, including increased stress, disrupted sleep, and increases in workfamily conflict (Sonnentag et al., 2008). Other studies have shown that there are positive effects of actively detaching-or recovering-from work during non-work hours (Sonnentag et al., 2012). We discuss recovery from work in more detail in Chapter 13. ...
... With increased feelings of vitality and enthusiasm, individuals are more likely to strive to engage themselves fully in their roles (Rich et al., 2010) across different domains in their lives, and they have more capacity to balance the demands of work with those of life. Prior research (Sonnentag et al., 2012) has suggested that individuals who have increased relational energy and enhanced psychological resources are better able to cope with workplace issues such as stress and burnout, thus improving their daily lives (Tang et al., 2021) and supporting their lives overall (Ten Brummelhuis & Greenhaus, 2018). These arguments support the notion that relational energy is highly likely to promote work-life balance. ...
Work‒life balance is becoming increasingly important to employees in contemporary society, who face demands from the different roles that they play in life. Drawing on social contagion theory, this paper investigates how employees’ work‒life balance can be improved by examining relational perspectives of leadership and human energy. The findings from a three-wave survey of 431 employees demonstrate that relational leadership exerts a positive effect on employees’ work‒life balance through employees’ relational energy. Core self-evaluation strengthens the mediating effect of relational energy on the relationship between relational leadership and work‒life balance such that this mediating effect is stronger at high level of core self-evaluation. This paper offers both theoretical contributions by confirming the novel energizing pathway associated with employees’ work‒life balance and the corresponding boundary condition (i.e., employees’ core self-evaluation) and managerial implications pertaining to how employees’ work‒life balance can be enhanced through managers’ leadership practices.
... Hindering job demands refer to factors that hinder employees' growth and development (LePine et al., 2005) and that may cause employees to have maladaptive cognitions and behaviors, which lead them to employ avoidant coping strategies that impede the use of resources (Bakker & Demerouti, 2024;Sonnentag et al., 2012). For example, employees facing high levels of hindering job demands may perceive the development training emphasized by sustainable leaders as a burden, making work engagement difficult. ...
Sustainable leadership is important in fostering the long-term development of employees, organizations, and society, but few studies have explored its impact on employees’ sustainable motivational states and performance. Guided by the job demands–resources (JD–R) theory, we posit that work engagement is a motivational process that links sustainable leadership with employees’ in- and extra-role performance. Specifically, we investigate whether family–work conflict and work pressure serve as moderators shaping the effectiveness of sustainable leadership on employee outcomes. We conducted two studies to test our hypotheses: a time-lagged survey study involving 236 supervisor–subordinate dyads in a high-risk industry (Study 1), and an experience sampling method study comprising 1,610 weekly observations of 354 participants working in diverse (non-high-risk) industries (Study 2). Our findings consistently show that sustainable leadership is positively related to micro-level employee performance (i.e., in- and extra-role performance) and that work engagement mediates this relationship. For employees working in high-risk industries, lower levels of family–work conflict or work pressure enhance both the direct (indirect) impact of sustainable leadership on work engagement (employee performance through work engagement). For employees in general industries, lower levels of family–work conflict or higher levels of work pressure enhance both the direct (indirect) impact of sustainable leadership on work engagement (employee performance through work engagement). This study expands the application of both sustainable leadership and JD–R theory and offers valuable recommendations for the sustainable development of employees and organizations in diverse industries.
... For example, research has shown that employees were less engaged on days they experienced greater levels of hindrance stressors (Breevaart and Bakker 2018). Furthermore, recovering from stress has been associated with greater engagement at work (Sonnentag et al. 2012). ...
Despite consistent findings that stressed employees benefit from social support, these employees do not always have access to such support. We propose and test a conceptual model suggesting employee work stress will negatively affect supervisory career and psychosocial mentoring support. Drawing from social exchange theory, we predict this will indirectly affect employee career success (lower career satisfaction and promotability ratings, fewer promotions), and that the relationship between employee work stress and lower supervisory mentoring support can be explained by lower levels of work engagement experienced by, and attributed to, stressed employees. We tested our model across three studies. In Study 1, we collected four waves of multisource field data (254 employees, 127 managers, and company records) at a large postal organization in the United Kingdom (UK). Employee work stress was negatively related to supervisor career and psychosocial mentoring support, and indirectly affected career satisfaction and manager promotability ratings of employees via supervisor career mentoring support. Cross‐lagged panel analyses in a supplemental study additionally supported the proposed directionality of relationships. Study 2 included data across three waves from employees in Hong Kong (n = 137) and showed that employee work stress had indirect effects on supervisor career and psychosocial mentoring via lower employee engagement. In Study 3, using data from supervisors in the UK (n = 240) we showed that supervisor perceived employee stress had indirect effects on their provision of supervisor career and psychosocial mentoring support via lower perceived employee engagement.
... For instance, Bakker et al. (2004) found that high job demands and a lack of job resources were significant predictors of burnout. Sonnentag et al. (2012) showed that chronic exposure to high job demands and low job control was associated with higher levels of exhaustion and disengagement. Demerouti et al. (2019) examined burnout among pilots, highlighting the importance of the work environment's psychosocial factors for their health and performance. ...
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... If recovery is insufficient, the neurobiological system will remain activated and will not return to a state of homeostasis (37). Employees in their suboptimal form will have to make extra efforts to cope with the demands of the next job, potentially leading to further long-term fatigue (38). This may also provide an explanation for other scores in the low free time group that were below those in the high decent work group. ...
Background
This study used a person-centered approach to identify the specific performance of decent work in various groups to determine the heterogeneity of its five dimensions.
Method
The Decent Work Scale, Work Need Satisfaction Scale, Socioeconomic Status Scale, Job Satisfaction Scale, and Life Well-being Scale were used to conduct a network survey of organizations in various industries in Mainland China. A total of 1,000 questionnaires were distributed, and 780 valid responses were obtained.
Results
The results showed that the decent work of participants could be divided into three types: low salary, low free time, and high decent work. The results showed no significant difference in age among the groups, whereas the differences in socioeconomic status were significant. Welch’s test was used to determine differences in the positive outcomes of the three potential types of decent work, and the results showed significant differences in work need satisfaction, job satisfaction, and life well-being among all groups.
Conclusion
This study examined the characteristics of decent work more realistically, showing that decent work is not an all-or-nothing structure and that its intrinsic components should be flexibly combined according to the research background and purpose.
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... According to the Conservation of Resources Theory, individuals strive to acquire, build, and protect their resources [25,35]. Demandshielding activities can help individuals prevent loss of resources to counteract stress, taking time for relaxation as a demand-shielding activity provide them with resource recovery pathway [36]. Some empirical evidences have suggested that relaxation at work is associated with more vitality and less fatigue [37]. ...
... By focusing specifically on relaxation, our study aims to provide actionable insights that can inform strategies to enhance academic engagement and foster a conducive environment for innovation. Incorporating relaxation practices into medical training programs represents a proactive approach to managing stress and maintaining high levels of engagement [36,42]. It not only mitigates the adverse effects of stressors on academic engagement but also enhances the overall well-being and innovative capacities of students, making it a cornerstone of effective educational strategies in demanding academic settings. ...
... This is in line with the Conservation of Resources Theory [25,35], which emphasizes the importance of resource preservation and recovery in mitigating stress. Relaxation activities buffer and protect students from the depletion of psychological resources often caused by hindrance stressors [36]. Furthermore, the findings also indicated that the indirect effect of hindrance stressors on innovative behavior mediated by academic engagement, is less pronounced among those with higher levels of relaxation. ...
This study investigated the relationship between challenge-hindrance stressors and innovative behavior of medical postgraduates in China, examining the mediating role of academic engagement and the moderating effect of relaxation. Drawing from a sample of 437 medical postgraduates from three Chinese universities, our findings revealed that challenge stressors positively correlated with innovative behavior, while the direct relationship between hindrance stressors and innovative behavior was not statistically significant. Furthermore, academic engagement mediated the relationship between two types of stressors and innovative behavior. Challenge stressors enhanced academic engagement, which in turn fostered innovative behavior. Conversely, hindrance stressors were found to diminish academic engagement, which in turn indirectly limited innovative behavior. Additionally, relaxation was identified as a moderating factor that helped mitigate the negative effects of hindrance stressors on academic engagement and indirectly on innovative behavior. These results suggested that academic engagement as a mechanism played a pivotal role in determining how different stressors influenced innovative behavior, underscoring the need for stress management, particularly through relaxation techniques, to maintain high levels of academic engagement and innovative behavior. This study offers practical insights for medical education policymakers and educators in China, emphasizing the importance of balancing stressors and incorporating relaxation practices to enhance the innovative capabilities of medical postgraduates in demanding academic environments.
... For example, Bakker et al. (2014) found that employees with high levels of motivation tended to develop more effective attentional regulation strategies, which improved their performance on tasks that required sustained cognitive effort. Similarly, Sonnentag et al. (2010) indicated that persistence at work was related to a lower susceptibility to distractions and greater stability in attentional effort, which allowed employees to sustain a high level of productivity throughout the workday. ...
This study analyzes the relationship between Absorption, Dedication, and Vigor in the work environment, in order to understand how these engagement factors influence the employee experience within their organizations. The research is based on theoretical models on work commitment and organizational well-being, considering that a higher level of dedication and vigor can enhance absorption at work. It is hypothesized that employees with high levels of dedication and vigor have higher levels of absorption, which suggests that work commitment is an interdependent construct where each dimension contributes to the strengthening of the others. To evaluate this relationship, a quantitative design based on multiple linear regression was used. The estimated econometric model shows that the coefficients of the variables Dedication and Vigor are positive and highly significant (p<0.001p < 0.001), indicating that the increase in these factors is associated with an increase in absorption at work. In addition, the F-statistic test suggests that the model is significant at 99% confidence, allowing the results to be interpreted with a high level of reliability. The findings obtained indicate that work engagement is built from dynamic interactions between its dimensions, which has important implications for the design of organizational strategies aimed at improving the employee experience. It is recommended that future research incorporate moderating variables, such as workload or organizational resilience, in order to better understand the mechanisms that explain this relationship.
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... Employees who perceive greater clarity in their roles experience lower levels of stress and a greater ability to direct their attention to tasks of high productive value. Previous studies have shown that organizational ambiguity increases mental fatigue and reduces the ability to concentrate, while clarity in internal communication strengthens cognitive efficiency and attentional stability (Sonnentag et al., 2010). ...
... For example, Ashforth et al. (2008) found that employees who reported greater clarity in the organizational structure of their companies showed better levels of attention and less tendency to make mistakes in tasks that required sustained concentration. Similarly, Sonnentag et al. (2010) indicated that lack of clarity in tasks increases stress levels and reduces the ability to focus on critical activities, which negatively affects organizational efficiency. ...
This study analyzes the relationship between Attention and Clarity in the organizational environment, analyzing how the perception of clarity in work processes can influence the ability of employees to maintain attention in their tasks. The research is based on theories on information processing, cognitive regulation and efficiency in performance, considering that clarity in organizational procedures and objectives facilitates concentration and reduces cognitive load. It is hypothesized that an increase in clarity in work processes is associated with an improvement in employee attention, suggesting that well-structured organizational environments can enhance individual performance. To evaluate this relationship, a quantitative design based on econometric techniques was used. The estimated simple linear regression model shows that the coefficient of the Clarity variable is positive and significant (p<0.001p < 0.001), indicating that employees who perceive greater clarity in their work environment tend to manifest a greater attention span in their activities. In addition, the model meets the fundamental assumptions of regression, including tests of specification, linearity, and absence of autocorrelation, allowing for reliable interpretation of the results. The findings suggest that organizational clarity is a factor that influences work care, which has implications in the design of organizational strategies aimed at performance optimization. It is recommended that future research extend the analysis by incorporating moderating variables such as task complexity and workload, in order to better understand the mechanisms underlying this relationship