Employee–supervisor political skill congruence and work outcomes: The mediating role of leader–member exchange quality

This study aims to investigate the effect of employee–supervisor political skill congruence on the quality of leader–member exchange (LMX), which in turn influences employees' job satisfaction and turnover intention. For this purpose, two survey studies were conducted. The first study examined the relationship between employee–supervisor political skill congruence and LMX using multi-source data collected from 287 employee–supervisor dyads. The second study examined the indirect effect of congruence in political skill on work outcomes through LMX by using time-lagged multi-source data collected from 142 employee–supervisor dyads. The findings indicate that compared with incongruence, congruence in political skill is associated with higher employee-rated LMX. Moreover, among dyads with congruent political skills, congruence at high levels of political skill is associated with higher employee-rated LMX compared with congruence at low levels of political skill. Moreover, in case of incongruency, low employee–high supervisor political skill combination was related to higher supervisor-rated LMX compared with high employee–low supervisor political skill combination. Lastly, employee–supervisor political skill congruency indirectly influences job satisfaction and turnover intention through employee-rated LMX. This research significantly contributes to the political skill literature by examining potential consequences of employee–supervisor congruence in political skill in the workplace.     

Examining the interactive effects of coach-created empowering and disempowering climate dimensions on athletes’ health and functioning

Based in Duda’s (2013) hierarchical and multidimensional conceptualisation of the motivational climate, the purpose of this study was to examine whether a coach-created empowering motivational climate moderated the debilitating effects of a disempowering motivational climate on athletes’ health and optimal functioning. Athletes (N = 406, M age = 23.1 years; 67% male) completed questionnaires assessing their perceptions of coach-created empowering and disempowering climates created in training and competition, enjoyment in sport, burnout symptoms, global self-worth, and symptoms of physical ill-health. Following the recommendations of Hayes (2013) and Dawson (2014), and using PROCESS (Hayes), moderated regression analyses showed that the interaction between disempowering and empowering climate dimensions was significant and predicted 1% unique variance in 3 outcome variables (i.e., enjoyment, reduced accomplishment, and physical symptoms). The Johnson-Neyman technique was employed to plot and probe the significant interactions, which revealed moderately strong to strong values of an empowering climate tempered the significant relationship between a disempowering climate and the three outcome variables. The findings from this study have implications for coach education and suggest programmes that train coaches to understand how to create empowering climates and avoid (or dramatically reduce) disempowering climates are warranted.     

Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the model are not directly related to latent classes. Results indicate that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. In addition, we tested whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a reanalysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted.