Leading in: inclusive leadership, inclusive climates and sexual harassment

PurposeThe purpose of this paper is to present a model that explores the relationship between inclusive leadership, inclusive climates and sexual harassment and other negative work-related outcomes, at the work unit and individual levels.Design/methodology/approachA conceptual model of inclusive work unit leadership, inclusive work unit climate and sexual harassment based on a review of the literature.FindingsLeaders who behave more inclusively are expected to have work units and work unit members who experience more positive outcomes and fewer negative outcomes including sexual harassment and other forms of mistreatment. Leaders impact their work unit and work unit members' outcomes directly as well as indirectly through the more inclusive work unit climates they create.Research limitations/implicationsThe sexual harassment literature has identified climate for sexual harassment as a key predictor of sexually harassing behavior and its attendant negative outcomes. A focus on a broader inclusive climate, and inclusive leadership, may provide a richer understanding of the conditions under which sexual harassment and other forms of mistreatment occur and can be mitigated.Practical implicationsThis model can help identify strategies organizations can employ (e.g. inclusive leadership development programs) to combat sexual harassment.Social implicationsThis model may improve understanding of the systemic, organizational causes of sexual harassment reducing sexual harassment victims' potential self-blame and helping policymakers craft more effective sexual harassment interventions.Originality/valueThe paper conceives of work climates that contribute to sexual harassment more broadly than generally has been the case in the sexual harassment literature to date. The model highlights the important role that leaders play in shaping inclusive climates. It also contributes to the nascent literature on inclusion and inclusive climates, which has paid relatively little attention to exclusion and mistreatment including sexual harassment that are likely to arise in less inclusive workplaces.

Multilevel Modeling Methods

Human behavior takes place in different contexts (e.g., organizations, schools, families, sports teams, and communities) whose properties (e.g., climate, culture, cohesion, leadership, communication networks, and structure) influence human behavior. To estimate this influence, researchers need appropriate methods that avoid the problems associated with the application of standard Ordinary Least Square (OLS) regression. Multilevel modeling methods offer researchers a way to estimate the aforementioned influence. These methods take into account that the variables involved reside at different levels. For instance, in the relationship between work unit climate and employee job satisfaction, the former variable resides at the work unit level (level 2) whereas the latter resides at the individual one (level 1). Moreover, multilevel modeling methods also take into account that the data analyzed to estimate this type of relationships have a nested structure in which individuals (e.g., employees) are nested into collectives (e.g., work units). Finally, these methods decompose variance into between-group and within-group components and allow researchers to model variability at the between and within levels. Specifically, multilevel modeling methods allow researchers to test hypotheses that involve, among others: 1. A relationship between a higher-level predictor (e.g., work unit climate) and a lower-level outcome (e.g., employee job satisfaction); a so-called “direct cross-level effect”, and 2. An influence of a higher-level moderator (e.g., work unit climate) on an individual level relationship (e.g., the relationship between employee job stress and job satisfaction); a so-called “cross-level interaction”. Multilevel modeling methods can also be used to test more complex models involving mediation (e.g., 2-2-1, 2-1-1, or 1-1-1 models, depending on whether the antecedent and the mediator are level 1 or level 2 variables) and moderated mediation. We show how to test these models by presenting examples with real data and the corresponding SPSS syntax that readers can use to practice.

Natural Occurrence and Co-Contamination of Twelve Mycotoxins in Industry-Submitted Cool-Season Cereal Grains Grown under a Low Heat Unit Climate Condition

This study aims to evaluate the prevalence of mycotoxins in industry-submitted cool-season barley and wheat grown under low heat unit climate conditions. Seventy-two barley samples and 83 wheat samples were submitted by producers and industry from May 2016 to May 2017. The concentrations of twelve common mycotoxins, including nivalenol (NIV), deoxynivalenol (DON), 3-acetyldeoxynivalenol (3-ADON), 15-acetyldeoxynivalenol (15-ADON), ochratoxin A (OTA), zearalenone (ZEN), α-zearalenol (α-ZAL), β-zearalenol (β-ZAL), diacetoxyscirpenol (DAS), T-2 toxin (T-2), HT-2 toxin (HT-2), and aflatoxin B1 (AFB1), were determined using the liquid chromatography/tandem mass spectrometry method. Mycotoxins were detected in 40 barley (56%) and 35 wheat (42%) samples submitted by producers and industry. DON showed the highest incidence in barley (44%) and wheat (33%). None of the barley samples contained detectable DAS and no wheat samples tested positive for α-ZAL, DAS, T-2, or AFB1. Co-occurrence of DON and other mycotoxins was frequently observed. Among the mycotoxin-positive samples, 70% of barley samples and 54% of wheat samples were co-contaminated with at least two mycotoxins. Four barley (6%) and five wheat (6%) samples contained levels of DON above 1000 μg/kg (regulatory level in diets for lactating dairy animals) and HT-2 content in five barley (7%) and four wheat (5%) samples exceeded 100 μg/kg (regulatory level in diets for cattle and poultry). Overall, contamination of these mycotoxins was more frequent and more severe in barley in comparison with wheat that was submitted by producers and industry. Comprehensive strategies, including the prevention of Fusarium toxins contamination, the routine monitoring of their prevalence, the detoxification of them in feed and food, as well as the inhibition of their absorption in the gastrointestinal tract, are highly required. A rapid detection method needs to be developed to screen mycotoxins in industry-submitted cool-season cereal grains.

Government Employees’ Organizational Citizenship Behavior Amid Organizational Resource Decline: Can They Work More With Less?

Based on the implication of the job demands–resources (JD-R) model, this study examined the associations between job demands (organizational resource declines and work overload) and resources (job-goal specificity, performance feedback, and work unit climate) with employees’ organizational citizenship behavior (OCB). Although statistically significant negative associations were found between financial and human resource decline and OCB, the associations were weak from a practical perspective. In line with the JD-R model, this study also found that job-goal specificity, performance feedback, and work supervisor support had positive associations with OCB. However, the effect of work overload was found to be marginalized, and the expected buffering role of job resources on the negative association of work overload with OCB was not confirmed in this study.