How Does Schema Affect Stress and Productivity at the Workplace?

Maintaining mental health has become a great concern not only for one's well-being, but also for companies' management, while one's personality trait has gained its popularity as a cause. In this article, the authors then investigate how worksite productivity loss is accounted for by stress response accompanied with schema. To this end, the conventional stress model is extended so as to include schema. The pivotal idea of the extended model stands in that both stressor and strain are associated with schema. The result of multiple regression analysis showed that workplace productivity loss is most largely affected by irritability, fatigue, and depression. In addition, the result of hierarchical regression analysis revealed that schema affects stress reaction both directly and indirectly, and that there exists a so-called buffering effect between job control and coworkers' support. These findings suggest that work productivity may be improved by intervention regarding schema or by promoting the buffering effect.

Quantifying the Evidence for the Absence of the Job Demands- and Job Control-Interaction on Workers' Well-Being: A Bayesian Meta-Analysis

Central theories on occupational health postulate that job resources reduce the negative effect of job demands on workers' well-being. However, this postulation lacks support thus far; numerous empirical investigations have assessed several job resources and several workers' well-being but studies produced inconsistent results. Systematic reviews tried to summarize these inconsistencies, however, no studies could adequately synthesize the breadth of studies to derive an aggregated conclusion due to missing quantitative aggregation. This study evaluates the interaction between job control and job demands on well-being by applying a systematic literature search and quantifying results through a Bayesian meta-analytic approach. Both aggregated study findings and raw participant-level data were included in our study, resulting in 104 effect sizes of aggregate-level data and 14 participant-level datasets. Overall, the data provided strong evidence for the absence of the interaction between job demands and control. Longitudinal and non-linear research designs were also examined but did not alter this overall conclusion. Contrary to the postulations of wide-spread theories, job control does not reduce the negative impact of job demands on worker well-being. Alternative theoretical approaches and the need for more consistent and rigorous research standards, like open-science practices, are discussed.

How job resources influence employee productivity and technology-enabled performance in financial services: the job demands–resources model perspective

PurposeThis study aims to provide insight into the relationship between job resources (job control and possibilities for development at work) and employee performance, measured as employee productivity and technology-enabled performance, by examining the role of employee well-being (work engagement and emotional exhaustion).Design/methodology/approachThe data comprised two overlapping data sets collected from a large financial institution; Study 1 employed survey data (N = 636), whereas study 2 employed register data on job performance collected over a one-year period combined with survey data (N = 143). The data were analysed through structural equation modelling.FindingsStudy 1 indicated that job resources were positively associated with technology-enabled performance more strongly through work engagement than emotional exhaustion. Study 2 revealed that emotional exhaustion was associated with lower employee productivity, whereas work engagement was not. Furthermore, the results indicated that job control was related to higher productivity through a lower level of emotional exhaustion.Practical implicationsThe study's findings point to the importance of developing interventions that decrease emotional exhaustion.Originality/valueThis is one of the first studies to measure employee productivity longitudinally as a ratio of inputs (working time) to outputs (relevant job outcomes) over one year. This study contributes to the job demands–resources model (JD-R) literature by showing the importance of job control in fostering both employee productivity and more positive perceptions of technology.

Job characteristics, well-being and physical activity: A field study using a consumer fitness tracker

The relation between job characteristics and health is one of the most important fields of research within work and organizational psychology. Another prominent variable influencing health is physical activity. The physical activity mediated Demand-Control (pamDC) model (Häusser & Mojzisch, 2017, https://doi.org/10.1080/02678373.2017.1303759) combines these health indicators in a new theoretical framework. Based on the pamDC model the current study aims to clarify the role of leisure time physical activity (LTPA) in the interplay of job demands, job control and well-being. We expect physical activity to partially mediate the impact of job characteristics on health. To avoid self-report bias considering physical activity we used a consumer fitness tracker to collect additional data. In total, 104 white-collar workers participated in the study. The results show that job control and job demands could predict well-being in cross-sectional analyses. In longitudinal analyses, this was only the case for job demands. Regarding the proposed mediating effect of LTPA between job characteristics and health, we could not detect a significant mediation in our sample. This was true for both self-reported and objective data on physical activity. This study provides a first step in validating the pamDC model and has implications for future research.

Specificity of Task Scheduling Algorithms in Geographically Distributed Computer Systems

The work is devoted to the consideration of the main widespread algorithms for scheduling tasks in geographically distributed computing systems. The specific features of the algorithms are characterized and their comparative analysis is presented in accordance with the selected criteria. The main factors that should be taken into account when constructing job control algorithms in geographically distributed computing systems are determined.

Individual Determinants of Rest-Break Behavior in Occupational Settings

Aims: Work breaks improve well-being, productivity, and health. The aim of this study was to investigate the individual determinants of rest-break behavior during work using the theory of planned behavior (TPB). Methods: The association between attitude, control, and subjective norm and rest-break intention (i.e., taking rest breaks regularly), and rest-break behavior (average number of rest breaks/workhour) was analyzed with stepwise linear regression in a cross-sectional design. The study participants included 109 clerical employees, and 215 nurses. Results: Attitude and control were positively associated with rest-break intention. Intention and control were positively associated with rest-break behavior. The effect of intention was moderated by occupation, with intention being more weakly associated with rest-break behavior in nurses who had less behavioral control. Conclusions: Job control is the major predictor of rest-break behavior, with attitudes playing a minor role, and social norm playing no role. To increase rest-break behavior, a greater extent of job control is necessary.

The Association of Job and Family Resources and Demands with Life Satisfaction through Work–Family Balance: A Longitudinal Study among Italian Schoolteachers during the COVID-19 Pandemic

Successfully balancing between work and family domains represents a major issue to both employees and employers, especially during COVID-19 pandemic times during which employees are often forced to work from a distance and turn to home-schooling. An occupational group particularly affected by work changes due to COVID-19 pandemic restrictions is represented by schoolteachers. We aimed at examining the associations between some job-related and family-related antecedents on the one hand and, on the other, life satisfaction as an outcome, including work–family balance as a mediator. A total of 357 Italian teachers completed a questionnaire at two different times: job control, coworkers support, supervisor support, workload, family support, and family workload were assessed at Time 1; and work–family balance and life satisfaction were assessed at Time 2. Both data collections were performed during the COVID-19 pandemic. The hypothesized direct and indirect relationships were tested by utilizing structural equation modeling. Significant and positive indirect effects of focal predictors towards life satisfaction through work–family balance were found for job control, supervisor support, and family support. The paper contributed to the literature by testing Grzywacz and Carlson’s theoretical conceptualization of work–family balance and by attempting to delineate its repertoire of potential antecedents among schoolteachers. From a practical point of view, the present study emphasizes the crucial role that certain job antecedents and family antecedents play in promoting teachers’ work–family balance and life satisfaction.

Working conditions and mental health functioning among young public sector employees

Background: The associations between adverse working conditions and mental disorders are well established. However, associations between adverse working conditions and poor mental health functioning is a less explored area. This study examines these associations among younger public sector employees of the City of Helsinki, Finland. Methods: We use data from the Young Helsinki Health Study with a representative sample of the employees of the City of Helsinki, aged 19–39 years ( n=4 217). Mental health functioning was measured with mental composite summary of the Short Form 36. Working conditions included factors related to both the psychosocial (job control and job demands) and the physical work environment (physical workload). To examine the associations, we used logistic regression models with adjustments for socio-demographics, other working conditions and health-related covariates. Results: After adjustment for sociodemographic characteristics, poor health, health behaviours and other occupational exposures, high job demands (OR=1.69; 95% CI=1.45–1.97) and low job control (OR=1.65; 95% CI=1.40–1.94) were associated with poor mental health functioning. High physical workload was not associated with the outcome (OR=0.87; 95% CI=0.72–1.05) after the adjustments. Conclusions: Adverse psychosocial working conditions were associated with mental health functioning, whereas physical working conditions were not. As impaired functioning is likely to cause health-related lost productivity and can lead to work disability, further research and interventions with a balanced approach focusing on both psychosocial working conditions and mental health functioning are recommended.