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In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

We develop a conceptual model, based on person-environment fit theory, which explains how employee age affects occupational strain and well-being. We begin by explaining how age directly affects different dimensions of objective and subjective P-E fit. Next, we illustrate how age can moderate the relationship between objective P-E fit and subjective P-E fit. Third, we discuss how age can moderate the relationships between P-E fit, on one hand, and occupational strain and well-being on the other. Fourth, we explain how age can impact occupational strain and well-being directly independent of P-E fit. The chapter concludes with implications for future research and practice.

This paper makes a contribution to the discussion on micro-foundations of dynamic capabilities – actions and interactions in organizations that enable continuous organizational renewal. More specifically, we propose the idea that dynamic capabilities of an organization are a positive function of corresponding dynamic capabilities of individual and collective actors in the organization. Further, we develop the assumption that not only individual acts of managers but also those of individuals and teams without managerial responsibility relate to dynamic capabilities of the organization. Following a holistic view, we also take into consideration empowering working conditions as an enhancing factor of this function. To examine these roots of dynamic capabilities, we use a multi-level model of competence provided by Wilkens, Keller, and Schmette (2006) that operationalizes the concept of dynamic capabilities provided by Teece (2007) on a concisely behavioral base. We investigated our hypotheses with a standardized questionnaire in a case study of a German plant engineering company with 112 participants and found primary support for our assumptions. Our results show an impact of individual dynamic capabilities on dynamic capabilities of the organization that is mediated by team dynamic capabilities. Psychological and social–structural empowerment moderated this relationship. A case-specific interpretation and implications for future research and practice are discussed.

Addressing occupational stress and fostering employee wellness helps meet a host of organizational stakeholder expectations including high quality of work life (employees), reasonable return on investment (investors), increased productivity (management), and competitiveness (owners). Despite being dynamic in nature, stress and wellness are often studied using a static perspective. One reason for the scarcity of dynamic empirical research is the limited knowledge and use of the tools available to assess change over time. To address this limitation, four tools used to assess change and dynamics of occupational stress and well-being are described: growth models, latent change score models, spectral analysis, and computational modeling. First, we begin by discussing growth curve models and then transition to latent change score models. We then expand into spectral analysis, a tool used to determine cycles of ups and downs that repeat regularly. Last, computational modeling is discussed, where computers and simulations are used to understand a dynamic process. For each tool, we give examples of how they have been used, make recommendations for future use, and provide readers with suggestions and references for how to complete analyses in software and programs, most of which are freely available (i.e., R, Vensim).

This chapter combines quantitative studies of the connections between stressors and performance in accounting settings and identifies the mediators and moderators of stressors–performance relationships. Using meta-analyses and path analyses, this research compiles 72 studies to investigate the relationships of stressors with accountant and auditor performance. As hypothesized, bivariate meta-analyses results indicate that work-related stressors negatively affect performance, and burnout and stress are negatively related to performance, whereas motivation is positively related to performance. Moreover, a meta-analytical structural equation modeling indicates that role stressors have significant direct and indirect effects (through burnout and stress) on job performance. Accumulation of multiple samples through meta-analysis bolsters statistical power compared to single-sample studies and thus reveals the sign of residual direct effects of role stressors on job performance in accounting settings.

There were 374 mass shootings in the United States between 2013 and 2014 and they were not random occurrences. We expanded the FBI’s definition of mass shootings (four or more injured or killed, not including the shooter), and collected data on every mass shooting that occurred in the United States to observe geographical patterns. Social Disorganization theories state that violence will occur in areas with high levels of poverty, large population density, and little economic opportunity. These theories work well with strain theory, which suggests that blocked goals, the introduction of a negative stimulus, or the removal of a positive stimulus leads to negative emotions, and these emotions can lead to crime in order to resolve the strain felt. From under this framework, we discover with point pattern analysis that there are patterns in the location of mass shootings crime scenes. They are not random. These crimes were far more likely to occur in the South, the Upper Midwest, and in Southern California, while they were considerably less likely to occur in the Pacific Northwest, Upper Midwest, or the Northeast. The lack of random spatial pattern indicates that the structural factors that contribute to mass shootings are more prevalent in certain areas of the United States than in others.

The preponderance of studies that rely on self-report for both independent (e.g. stressors) and dependent (e.g. well-being) variables is often deplored, as it creates problems of common method variance, which may lead to inflated, or even spurious, correlations and predictions. It is sometimes suggested that alternative measures should yield more “objective” information on the phenomena under investigation. We discuss this issue with regard to: (a) observational measures of working conditions; (b) physiological measures of strain; and (c) event-based “self-observation” on a micro-level. We argue that these methods are not necessarily “objective.” Like self-report, they are influenced by a plethora of factors; and measurement artifacts can easily be produced. All this can make their interpretation quite difficult, and the conclusion that lack of convergence with self-report automatically invalidates self-report is not necessarily warranted. Especially with regard to physiological measures, one has to keep in mind that they refer to a different response level that follows its own laws and is only loosely coupled with psychological responses. Therefore, replacement is not a promising way to get more reliable estimates of stressor-strain relationships. We argue instead that each method contains both substantive and error variance, and that a combination of various methods seems more auspicious. After discussing advantages and pitfalls of observational, physiological, and self-observational measures, respectively, we report empirical examples from our own research on each of these methods, which are meant to illustrate both the advantages and the problems associated with them. They strengthen the overall conclusion that there is no “substitute” for self-report (which often is necessary to be able to interpret data from other methods, most notably physiological ones). They also illustrate that collecting such data is quite cumbersome, and that a number of conditions have to be carefully considered before using them, and we report some problems we encountered in this research. Altogether, we conclude that self-report measures, if carefully constructed, are better than their reputation, but that the optimal way is to complement them with other measures.