Lei Huang

We draw from ego depletion and leader–member exchange theories to provide nuanced insight into why abusive supervision is indirectly associated with supervisor-directed destructive voice. A multi-wave, multi-source field study demonstrates evidence that abusive supervision has a positive conditional indirect effect on supervisor-directed destructive voice through subordinates’ relational ego depletion with their supervisors that is stronger for higher LMX differentiation contexts than lower LMX differentiation contexts. We make novel theoretical, empirical, and practical contributions by providing a parsimonious explanation for why relational aspects of supervisory treatment drain subordinates’ capacities for controlling their volitional actions during interactions with their supervisors and how this relationship impacts subordinates’ supervisor-directed destructive voice. Overall, our study extends the application of ego depletion and LMX theories to the examination of abusive supervision and destructive voice in order to meaningfully inform researchers’ attempts to build cohesive streams of research in these areas and practitioners’ attempts to promote ethical workplace environments.

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gated Recurrent Unit as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation.