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Crises, such as the COVID-19 pandemic, require rapid action to be taken by leaders, despite minimal understanding of the impact of implemented crisis management policies and procedures in organizations. This study's purpose was to establish a greater understanding of which perceived crisis response strategies were the most beneficial or detrimental to relevant perceptions and outcomes during the recent COVID-19 crisis. Using a time-lagged study design and a sample of 454 healthcare employees, latent profile analysis was used to identify strategy profiles used by organizations based on several policy/procedure categories (i.e., human-resource supportive, human-resource disadvantaging, behavioral/interactional human safety and protection-focused, and environmental and structural safety supports-focused policies and procedures). Results indicated that four perceived crisis response strategies were employed: (1) human resource-disadvantaging, (2) maximizing, (3) safety and human resource-supportive, and (4) inactive. Perceived crisis response strategy was linked to several employee well-being (e.g., work stress) and behavioral (e.g., safety behavior) outcomes via proximal perceptions (i.e., perceived organizational support, ethical leadership, and safety climate). Proximal perceptions were the most positive for employees within organizations that enacted safety and human resource-supportive policies and procedures or that utilized a maximizing approach by implementing a wide array of crisis response policies and procedures. This paper contributes to the literature by providing crucial information needed to reduce organizational decision-making time in the event of future crises.

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Despite widespread use of forced distribution rating systems (FDRSs), the potential for this performance appraisal method to lead to adverse impact (AI) in a layoff context has yet to be examined empirically. Thus, the current study uses a Monte Carlo simulation to examine the likelihood of encountering AI violations when an FDRS is used in the context of layoffs. The primary research questions included an examination of how AI violations change depending on the definition of the employment action (i.e., retention vs. layoff), the length of the repeated layoffs, and whether or not laid off employees are replaced each year. The current study also examined the impact of the size of the organization, the percentage of the workforce laid off, and the type of AI calculation method used on the likelihood of AI violations. Results suggest that defining the employment action as layoffs (rather than as retentions) may result in a greater likelihood of AI violations, and AI violations are likely to peak in the 1st year of use. Further, replacing laid off employees may result in higher levels of AI over time as compared with not replacing layoffs. Additionally, the greatest risk for AI occurs when the organization size is large (i.e., N = 10,000) and when certain AI calculation methods are used. Results are discussed in terms of their practical and legal implications for organizations.

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Although the Internet has transformed the way our world operates, it has also served as a venue for cyberbullying, a serious form of misbehavior among youth. With many of today's youth experiencing acts of cyberbullying, a growing body of literature has begun to document the prevalence, predictors, and outcomes of this behavior, but the literature is highly fragmented and lacks theoretical focus. Therefore, our purpose in the present article is to provide a critical review of the existing cyberbullying research. The general aggression model is proposed as a useful theoretical framework from which to understand this phenomenon. Additionally, results from a meta-analytic review are presented to highlight the size of the relationships between cyberbullying and traditional bullying, as well as relationships between cyberbullying and other meaningful behavioral and psychological variables. Mixed effects meta-analysis results indicate that among the strongest associations with cyberbullying perpetration were normative beliefs about aggression and moral disengagement, and the strongest associations with cyberbullying victimization were stress and suicidal ideation. Several methodological and sample characteristics served as moderators of these relationships. Limitations of the meta-analysis include issues dealing with causality or directionality of these associations as well as generalizability for those meta-analytic estimates that are based on smaller sets of studies (k < 5). Finally, the present results uncover important areas for future research. We provide a relevant agenda, including the need for understanding the incremental impact of cyberbullying (over and above traditional bullying) on key behavioral and psychological outcomes.

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Heteroscedasticity refers to a phenomenon where data violate a statistical assumption. This assumption is known as homoscedasticity. When the homoscedasticity assumption is violated, this can lead to increased Type I error rates or decreased statistical power. Because this can adversely affect substantive conclusions, the failure to detect and manage heteroscedasticity could have serious implications for theory, research, and practice. In addition, heteroscedasticity is not uncommon in the behavioral and social sciences. Thus, in the current article, we synthesize extant literature in applied psychology, econometrics, quantitative psychology, and statistics, and we offer recommendations for researchers and practitioners regarding available procedures for detecting heteroscedasticity and mitigating its effects. In addition to discussing the strengths and weaknesses of various procedures and comparing them in terms of existing simulation results, we describe a 3-step data-analytic process for detecting and managing heteroscedasticity: (a) fitting a model based on theory and saving residuals, (b) the analysis of residuals, and (c) statistical inferences (e.g., hypothesis tests and confidence intervals) involving parameter estimates. We also demonstrate this data-analytic process using an illustrative example. Overall, detecting violations of the homoscedasticity assumption and mitigating its biasing effects can strengthen the validity of inferences from behavioral and social science data.

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Existing research on workplace incivility has demonstrated an association with a host of negative outcomes, including increased burnout, turnover intentions, and physical symptoms. With the rise in Internet communication over the last decade, interpersonal mistreatment has spilled over to the Internet, but little is known about the impact of incivility communicated via e-mail on employee psychological and performance outcomes. The current study presents a within-subjects experiment wherein incivility and support were manipulated in a laboratory-based simulated workplace setting. Eighty-four participants completed a series of math tasks while interacting with either an uncivil or a supportive supervisor via e-mail. Data were collected on energy, cardiac activity, mood, task performance, and engagement. Findings indicate that participants reported higher levels of negative affect and lower levels of energy after working with the uncivil supervisor than with the supportive supervisor. Additionally, participants performed significantly worse on the math tasks and had lower engagement in the uncivil condition than the supportive condition, and these relationships were mediated by energy. No differences were found in cardiac activity between the two conditions. Results are discussed in terms of their implications for the 21st century world of work.

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The current study was designed to extend the interpersonal deviance literature into the online domain by examining the incidence and impact of supervisor cyber incivility and neuroticism on employee outcomes at work. Conservation of Resources (COR) theory was used as the guiding framework because cyber incivility is thought to deplete energetic resources in much the same way that other stressors do, ultimately leading to negative outcomes like burnout. Results indicate that supervisor cyber incivility is positively related to burnout, absenteeism, and turnover intentions. Support was also found for the role of neuroticism as a moderator of the relationship between supervisor cyber incivility and outcomes. In general, the relations between cyber incivility and outcomes were stronger for those individuals reporting higher levels of neuroticism. Results are discussed in terms of COR theory, and possible mechanisms for the role of neuroticism in the stressor-strain relationship are discussed. The current study highlights the importance of understanding workplace online behavior and its impact on employee health and organizational well-being. Future research directions examining online interpersonal deviance are suggested.

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