Burnout as a State: Random-Intercept Cross-Lagged Relationship Between Exhaustion and Disengagement in a 10-Day Study

Using Ecological Momentary Assessments to Study How Daily Fluctuations in Psychological States Impact Stress, Well-Being, and Health

Despite great interest in how dynamic fluctuations in psychological states such as mood, social safety, energy, present-focused attention, and burnout impact stress, well-being, and health, most studies examining these constructs use retrospective assessments with relatively long time-lags. Here, we discuss how ecological momentary assessments (EMAs) address methodological issues associated with retrospective reports to help reveal dynamic associations between psychological states at small timescales that are often missed in stress and health research. In addition to helping researchers characterize daily and within-day fluctuations and temporal dynamics between different health-relevant processes, EMAs can elucidate mechanisms through which interventions reduce stress and enhance well-being. EMAs can also be used to identify changes that precede critical health events, which can in turn be used to deliver ecological momentary interventions, or just-in-time interventions, to help prevent such events from occurring. To enable this work, we provide examples of scales and single-item questions used in EMA studies, recommend study designs and statistical approaches that capitalize on EMA data, and discuss limitations of EMA methods. In doing so, we aim to demonstrate how, when used carefully, EMA methods are well poised to greatly advance our understanding of how intrapersonal dynamics affect stress levels, well-being, and human health.

Measuring digital transformation stress at the workplace-Development and validation of the digital transformation stress scale

Despite the unquestionable advantages of digital transformation (DT) in organizations, the very process of DT could have an impact on the level of stress of the employees. The negative effects of the digital transformation process can be observed during the implementation of information and communication technologies (ICT) solutions. They are further enhanced by the effects of COVID-19 pandemic, as digital transformation has accelerated to allow for remote work. Herein we distinguish between general stress at the workplace and the very specific type of stress, namely digital transformation stress (DTS). We assumed that this type of stress appears when rapid implementation of ICT solutions is introduced with time pressure and incertitude of further results. To quantify this phenomenon, we developed a new self-report scale-the Digital Transformation Stress Scale (DTSS), measuring employees' stress stemming from the process of digital transformation in organizations. The psychometric validity of the scale was evaluated in two studies: Study1 conducted at the beginning of COVID-19 pandemic in 2020 (N = 229) and Study 2 in 2021 (N = 558), after a year of mostly remote work. The results confirmed good reliability with Cronbach's Alpha α = .91 in Study 1 and α = .90 in Study 2 and assumed unidimensional factorial validity of the scale in both studies. All items of the scale had good difficulty and discrimination values evaluated in Item Response Theory, i.e., IRT approach. The scale showed predicted convergent validity as the indicator of the digital transformation stress moderately correlated with general stress at work. Moreover, the assumption that even employees with high ICT skills could be affected by DTS was confirmed. Additionally, the results indicated that digital transformation stress was significantly higher among employees who reported both issues: ongoing digital solutions projects at the workplace and high impact of COVID-19 pandemic on their work. The scale could be used in future work on measuring and counteracting digital transformation stress at the workplace.

Collinearity Issues in Autoregressive Models with Time-Varying Serially Dependent Covariates

First-order autoregressive models are popular to assess the temporal dynamics of a univariate process. Researchers often extend these models to include time-varying covariates, such as contextual factors, to investigate how they moderate processes' dynamics. We demonstrate that doing so has implications for how well one can estimate the autoregressive and covariate effects, as serial dependence in the variables can imply predictor collinearity. This is a noteworthy contribution, since in current practice serial dependence in a time-varying covariate is rarely considered important. We first recapitulate the role of predictor collinearity for estimation precision in an ordinary least squares context, by discussing how it affects estimator variances, covariances and correlations. We then derive a general formula detailing how predictor collinearity in first-order autoregressive models is impacted by serial dependence in the covariate. We provide a simulation study to illustrate the implications of the formula for different types of covariates. The simulation results highlight when the collinearity issue becomes severe enough to hamper interpretation of the effects. We also show that the effect estimates can be biased in small samples (i.e., 50 time points). Implications for study design, the use of time as a predictor, and related model variants are discussed.

Within- and between-person factor structure of the Oldenburg Burnout Inventory: Analysis of a diary study using multilevel confirmatory factor analysis

The study examined the factor structure of burnout, as measured with the Oldenburg Burnout Inventory. The participants were 235 employees of a public administration agency who assessed their burnout online for 10 consecutive working days. Two models were tested with multilevel confirmatory factor analysis, assuming the same one or two-factor structure at the within- and between-person levels. Both models showed a reasonable fit to the data, but due to a strong correlation between exhaustion and disengagement and low within-person reliability for disengagement, a unidimensional model seems more valid. A cross-level invariance was not confirmed for either of the structures, showing that factor loadings for the same items differ significantly between the levels. This suggests that burnout is not the same latent variable at each level; rather, there are factors other than daily burnout that influence person-level scores and ignoring these across-level discrepancies may lead to biased conclusions.

Financial Insecurity During the COVID-19 Pandemic: Spillover Effects on Burnout-Disengagement Relationships and Performance of Employees Who Moonlight

The novel Coronavirus disease (COVID-19) has magnified the issue of financial insecurity. However, its effect on individual-organizational relations and, consequently, on organizational performance remains understudied. Thus, the purpose of this study was to explore the spillover effect of financial insecurity on the burnout-disengagement relationship during the pandemic. The authors investigate in particular whether the spillover effect influences the performance of moonlighting employees and also explore the mediating effect of disengagement on the relationship between financial insecurity and burnout interaction effect and the performance (i.e., mediated-moderation). This study collected responses from 162 public and private sector employees who are engaged in moonlighting activities in Malaysia. The results from the partial least square structural equation modeling (PLS-SEM) revealed greater levels of financial insecurity and burnout associated with greater levels of work disengagement. The analysis of the interaction-moderation effect showed that when financial insecurity rises, the burnout effect on work disengagement increases among moonlighters. Using the PROCESS macro model, the results displayed burnout as a predictor of extra-role performance via a moderated (financial insecurity) mediation (work disengagement) relationship. Going forward, this study not only opens new avenues for research into the financial consequences of COVID-19 but also calls on managers to take proactive steps to mitigate the negative effect of the pandemic on the performance of moonlighting employees to keep them in the profession.

Using Residual Dynamic Structural Equation Modeling to Explore the Relationships among Employees' Self-Reported Health, Daily Positive Mood, and Daily Emotional Exhaustion

This study examined the relationships among self-reported health, daily positive mood, and daily emotional exhaustion among employees in health and fitness clubs using residual dynamic structural equation modeling (RDSEM). A questionnaire was completed by 179 employees at recruitment and then a diary survey over 10 consecutive workdays. Results of RDSEM analyses revealed that daily positive mood was negatively associated with daily emotional exhaustion at both within-person and between-person levels. Self-reported health was positively related to the person’s mean of daily positive mood and negatively associated with the person’s mean of daily emotional exhaustion. Self-reported health moderated the relationship between daily positive mood and daily emotional exhaustion; employees with higher self-reported health levels tend to respond with larger changes in their daily emotional exhaustion when their daily positive mood changes. These findings provide important insights for organizations aiming at their employees’ health, happiness, and job burnout.