West Virginia University

About: West Virginia University is a education organization based out in Morgantown, West Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 25632 authors who have published 48308 publications receiving 1343934 citations. The organization is also known as: WVU & West Virginia University, WVU.

Empowering Leadership in Management Teams: Effects on Knowledge Sharing, Efficacy, And Performance

Abstract: We surveyed management teams in 102 hotel properties in the United States to examine the intervening roles of knowledge sharing and team efficacy in the relationship between empowering leadership and team performance. Team performance was measured through a time-lagged market-based source. Results showed that empowering leadership was positively related to both knowledge sharing and team efficacy, which, in turn, were both positively related to performance.

Heterogeneous Graph Attention Network

Abstract: Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.

Encouraging Knowledge Sharing: The Role of Organizational Reward Systems

Abstract: This article examines the role of monetary rewards in encouraging knowledge sharing in organizations through four mechanisms of knowledge sharing. We argue that the system of contributing knowledge to databases is the most amenable to rewards contingent on knowledge sharing behaviors because of opportunities for the reward allocator to measure the knowledge sharing behaviors. In the case of formal interactions within or across teams and work units, while rewards could be made partly contingent on knowledge sharing behaviors as in merit pay, rewards based on collective performance are also likely to be effective in creating a feeling of cooperation, ownership, and commitment among employees. In addition, we propose that team-based rewards and company wide incentives (profit sharing, gainsharing, and employee stock options) would be particularly instrumental in enhancing knowledge sharing within teams and across work units, respectively. In the case of knowledge sharing through informal interactions, the ke...

Nonlocally Centralized Sparse Representation for Image Restoration

Abstract: Sparse representation models code an image patch as a linear combination of a few atoms chosen out from an over-complete dictionary, and they have shown promising results in various image restoration applications. However, due to the degradation of the observed image (e.g., noisy, blurred, and/or down-sampled), the sparse representations by conventional models may not be accurate enough for a faithful reconstruction of the original image. To improve the performance of sparse representation-based image restoration, in this paper the concept of sparse coding noise is introduced, and the goal of image restoration turns to how to suppress the sparse coding noise. To this end, we exploit the image nonlocal self-similarity to obtain good estimates of the sparse coding coefficients of the original image, and then centralize the sparse coding coefficients of the observed image to those estimates. The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, while our extensive experiments on various types of image restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed NCSR algorithm.