Although interest regarding the role of dispositional affect in job behaviors has surged in recent years, the true magnitude of affectivity's influence remains unknown. To address this issue, the authors conducted a qualitative and quantitative review of the relationships between positive and negative affectivity (PA and NA, respectively) and various performance dimensions. A series of meta-analyses based on 57 primary studies indicated that PA and NA predicted task performance in the hypothesized directions and that the relationships were strongest for subjectively rated versus objectively rated performance. In addition, PA was related to organizational citizenship behaviors but not withdrawal behaviors, and NA was related to organizational citizenship behaviors, withdrawal behaviors, counterproductive work behaviors, and occupational injury. Mediational analyses revealed that affect operated through different mechanisms in influencing the various performance dimensions. Regression analyses documented that PA and NA uniquely predicted task performance but that extraversion and neuroticism did not, when the four were considered simultaneously. Discussion focuses on the theoretical and practical implications of these findings. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

The job demands-control-support model (DCS; Karasek, 1979) is an influential theory for understanding how work characteristics relate to employee well-being, health, and performance. However, previous research has largely neglected theory-building regarding the interrelationships between job demands, control, and support. We remedy such theoretical underdevelopment by reviewing and integrating theory on the relationships between demands, control, and support to develop five hypotheses. We test our hypotheses within a meta-analytic framework using a set of 106 studies. Our results show negative demands-supervisor support and demands-coworker support relationships, but no significant demand-control relationship. Our findings also indicate positive control-supervisor support and control-coworker support relationships. Using the meta-analytic effect sizes, we also estimate two competing structural equation models intended to discern which theoretical model using DCS work characteristics to predict occupational strain and well-being is more consistent with our data. Our results suggest that job control and both sources of social support should be treated independently, as opposed to indicators of a shared latent factor, in terms of their prediction of well-being and job demands. Our study offers support for the usefulness of the DCS and more modern conceptualizations of the working environment in understanding the employee work experience and for predicting important work outcomes. (

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Determining independent variable relative importance is a highly useful practice in organizational science. Whereas techniques to determine independent variable importance are available for normally distributed and binary dependent variable models, such techniques have not been extended to multicategory dependent variables (MCDVs). The current work extends previous research on binary dependent variable relative importance analysis to provide a methodology for conducting relative importance analysis on MCDV models from a dominance analysis (DA) perspective. Moreover, the current work provides a set of comprehensive data analytic examples that demonstrate how and when to use MCDV models in a DA and the advantages general DA statistics offer in interpreting MCDV model results. Moreover, the current work outlines best practices for determining independent variable relative importance for MCDVs using replicable examples on data from the publicly available General Social Survey. The present work then contributes to the literature by using in-depth data analytic examples to outline best practices in conducting relative importance analysis for MCDV models and by highlighting unique information DA results provide about MCDV models.

Dominance analysis is a common method applied to statistical models to determine the importance of independent variables. In this article, I describe two community-contributed commands, domin and domme, that can be used to dominance-analyze both independent variables and parameter estimates in Stata estimation commands. I discuss how to compute dominance statistics, provide multiple examples of each command applied to data, and outline how to interpret the results from each data-analytic example. I conclude with computational considerations for users applying larger models.