Although workplace harassment affects the lives of many employees, until recently it has been relatively ignored in the organizational psychology literature. First, the authors introduced an attribution- and reciprocity-based model that explains the link between harassment and its potential causes and consequences. The authors then conducted a meta-analysis to examine the potential antecedents and consequences of workplace harassment. As shown by the meta-analysis, both environmental and individual difference factors potentially contributed to harassment and harassment was negatively related to the well-being of both individual employees and their employing organizations. Furthermore, harassment contributed to the variance in many outcomes, even after controlling for 2 of the most commonly studied occupational stressors, role ambiguity and role conflict.

No abstract

The current meta-analysis examined the relationship between job satisfaction and subjective well-being (SWB). Consistent with the spillover hypothesis, we found positive relationships between job satisfaction and life satisfaction, happiness, positive affect, and the absence of negative affect. In addition, an examination of longitudinal studies suggested that the causal relationship from SWB to job satisfaction was stronger than the causal relationship from job satisfaction to SWB.

No abstract

Insufficient effort responding (IER; Huang, Curran, Keeney, Poposki, & DeShon, 2012) to surveys has largely been assumed to be a source of random measurement error that attenuates associations between substantive measures. The current article, however, illustrates how and when the presence of IER can produce a systematic bias that inflates observed correlations between substantive measures. Noting that inattentive responses as a whole generally congregate around the midpoint of a Likert scale, we propose that Mattentive, defined as the mean score of attentive respondents on a substantive measure, will be negatively related to IER's confounding effect on substantive measures (i.e., correlations between IER and a given substantive measure will become less positive [or more negative] as Mattentive increases). Results from a personality questionnaire (Study 1) and a simulation (Study 2) consistently support the hypothesized confounding influence of IER. Using an employee sample (Study 3), we demonstrated how IER can confound bivariate relationships between substantive measures. Together, these studies indicate that IER can inflate the strength of observed relationships when scale means depart from the scale midpoints, resulting in an inflated Type I error rate. This challenges the traditional view that IER attenuates observed bivariate correlations. These findings highlight situations where IER may be a methodological nuisance, while underscoring the need for survey administrators and researchers to deter and detect IER in surveys. The current article serves as a wake-up call for researchers and practitioners to more closely examine IER in their data.