This study explores how and when ethical leadership predicts three forms of team-level creativity, namely team creativity, average of member creativity, and dispersion of member creativity. The results, based on 230 members of 44 knowledge work teams from Chinese organizations, showed that ethical leadership was positively related to team creativity and average of member creativity but was negatively related to dispersion of member creativity. Consistent with the predictions of uncertainty reduction theory, psychological safety climate mediated the relationship between ethical leadership (...) and the three forms of team-level creativity. Furthermore, supervisor support for creativity positively moderated the effect of ethical leadership on psychological safety climate and the indirect effects of ethical leadership on the three forms of team-level creativity through psychological safety climate. The analysis offers significant theoretical and practical implications on ethical leadership and creativity in organizations. (shrink)

This study proposes a moderated mediation model based on deontic justice theory to investigate the impact of unethical peer behavior on employee performance. Data were collected in China through two survey studies, with two measurement points in each study. The data in study 1 were obtained from 271 employees of 17 firms, and the data in study 2 were collected from 225 employees of 9 firms. Confirmatory factor analysis was conducted to confirm the factorial validity of the measures employed in (...) the studies. The hypothesized moderated mediation model was tested using hierarchical multiple regression analysis. The results show that employee deontic justice mediates the negative relationship between unethical peer behavior and employee performance, and a stronger ethical climate tends to strengthen this indirect relationship. This study extends previous research by introducing deontic justice theory to explore the unconscious process that relates unethical peer behavior to employee performance. The theoretical and practical implications of this paper are discussed. (shrink)

Adolescents have gradually become a vital group of interacting with social media recommendation algorithms. Although numerous studies have been conducted to investigate negative reactions that the dark side of recommendation algorithms brings to social media users, little is known about the resistance intention and behavior based on their agency in the daily process of encountering algorithms. Focusing on the concept of algorithm resistance, this study used a two-path model to investigate the algorithmic resistance of rural Chinese adolescents in their daily (...) use of short video apps. The findings revealed that the perceived threat to freedom, algorithmic literacy, and peer influence were positively associated with the resistance willingness and intention; while the independent psychology on algorithmic recommendations significantly weakened resistance willingness and intention. Furthermore, this study verified the mediating role of resistance willingness and intention between the above independent variables and resistance behavior. Additionally, the positive impact of resistance willingness on resistance intention was confirmed. In conclusion, this study offers a comprehensive approach to further understanding adolescents’ algorithmic resistance awareness and behavior by combining psychological factors, personal competency, and interpersonal influences, as well as two types of resistance reactions. (shrink)

Aiming to realize rapid and efficient three-dimensional identification of substation equipment, this article proposes a new method in which the 3D identification of substation equipment is based on K-nearest neighbor classification of subspace feature vector. First of all, the article uses octree encoding to reduce and denoise the point cloud data obtained by a 3D laser scanner. Secondly, position calibration and size standardization are used for the point cloud after pretreatment. Then, the normalized point cloud is divided into a number (...) of cubes with same size. The cosine of the angle between the positive direction of z axis and a vector from the global centroid of the point cloud to the centroid of each subspace is regarded as the feature of the subspace. All cosines of subspaces constitute the feature of the point cloud. Finally, we classify the subspace feature vector by using the KNN algorithm and improve classification accuracy by using the particle swarm optimization algorithm. The simulation results show that the identification accuracy of the proposed method for unknown substation equipment is about 90% and the proposed method is applicable to low-degree losses. Apparently, this method can accurately identify 3D substation equipment. At the same time, increasing the number of subspaces will improve the accuracy; however, it will increase the recognition time. (shrink)

This study explores how the variability of the work environment shapes the impact of educational level diversity on team creativity. By adopting an integrative framework—“status characteristics–information elaboration” model as a theoretical lens, we propose and examine the moderating roles of task and personnel variability in educational level diversity–team creativity relationship. Utilizing multiple survey data collected from 90 knowledge work teams, the empirical results indicate that educational level diversity is more conducive to team creativity when teams are confronted with more variable (...) tasks and when teams experience less frequent personnel changes. The findings of this study provide valuable insight on the conditions under which team diversity’s information potential is more likely to realize and contribute to a more context-based understanding of the relationship between diversity and creativity. (shrink)

The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time-consuming. In order to address this problem, an infrared image generation method, infrared generative adversarial networks, based on conditional generative adversarial networks architecture is proposed. In I-GANs, visible images instead of random noise are used as the inputs, and the D-LinkNet network is also utilized to build the generative model, (...) enabling improved learning of rich image textures and identification of dependencies between images. Moreover, the PatchGAN architecture is employed to build a discriminant model to process the high-frequency components of the images effectively and reduce the amount of calculation required. In addition, batch normalization is used to optimize the training process, and thereby, the instability and mode collapse of the generated adversarial network training can be alleviated. Finally, experimental verification is conducted on the produced infrared/visible light dataset. The experimental results reveal that high-quality and reliable infrared data are generated by the proposed I-GANs. (shrink)

Self-contribution may be an influential factor in fairness consideration and consequent behavioral decisions. Few studies have investigated simultaneous effects of task difficulty and self-contribution on fairness consideration outcomes and associated neurophysiological responses. To elucidate modulation effects of task difficulty and self-contribution on fairness consideration, 30 recruited participants played a modified ultimatum game while undergoing event-related potential measurements. A 2 × 3 × 2 within-subject design was adopted. A significant interaction between fairness type and contribution was observed in the behavioral data, (...) with unfair offers being more acceptable in the other-contribution condition than in the self-contribution or both-contribution conditions. In the early processing time window, feedback-related negative magnitudes were greater in the hard condition than in the easy condition. P300 responses were more pronounced when participants contributed equally to the proposer than in the self- and other-contribution conditions. These results demonstrated that individuals’ decisions are influenced by their own effort contributions relative to those of others in cooperative contexts. (shrink)

The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples’ rapid collision (...) query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency. (shrink)