University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

The moderating effect of environmental munificence and dynamism on the relationship between discretionary social responsibility and firm performance

Abstract: This study examines the relationships between a company's emphasis on discretionary social responsibility, environment, and firm performance. It tests the proposition that environmental munificence and dynamism moderate the relationship between discretionary social responsibility and financial performance. Social responsibility was measured with a three-item scale in a sample of 62 firms using a questionnaire. Environmental munificence and dynamism were measured using archival sources as was financial performance (return on assets and return on sales). The results of moderated regression analyses and subgroup analyses found a significant moderating effect of environment on the social responsibility-firm performance relationship. Discretionary social responsibility contributes to firm performance in environments that are dynamic and munificent.

Internet versus Bricks-and-Mortar Retailers: An Investigation into Intangibility and Its Consequences

Abstract: Intangibility has long been studied as a unidimensional construct with the focus being placed upon the physical element. This paper explores the effects of three unique intangibility dimensions on a consumer’s ability to evaluate goods and services, and the perceived risk (PR) associated with the transaction. The authors examine these relationships in purchase environments that include both traditional bricks-and-mortar retailers and the Internet. Their investigation further incorporates prior knowledge as a moderating factor into the proposed framework. This allows for a thorough comparison of the effects and relationships that exist between intangibility and its consequences in general, evaluation difficulty (ED) and perceived risk (PR) in particular. The authors develop hypotheses pertaining to the proposed model and test them with two experiments. The empirical results are broadly supportive of the hypotheses. Theoretical and managerial implications to the services marketing literature are discussed.

Relationships between Top Management Team Characteristics and International Diversification: an Empirical Investigation*

Abstract: Drawing on the executive demography and the upper-echelons perspectives, this paper examines the relationships between top management team (TMT) characteristics (educational level, tenure, age, international experience and functional background) and firm international diversification. The study is based on a sample of 112 relatively large, internationally diversified US-based firms in the manufacturing sector. Findings indicate that firms with higher levels of international diversification are likely to have TMTs characterized by higher educational level, shorter organizational tenures, younger executives and greater international experience. In addition, findings indicate that the relationships between TMT characteristics and international diversification are more dominant in better-performing than in lower-performing firms.

Association of enjoyable leisure activities with psychological and physical well-being.

Abstract: Objective:To examine whether engaging in multiple enjoyable activities was associated with better psychological and physiological functioning. Few studies have examined the health benefits of the enjoyable activities that individuals participate in voluntarily in their free time.Method:Participants

Adaptive Graph Convolutional Neural Networks

Abstract: Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for each graph data while training. To efficiently learn the graph, a distance metric learning is proposed. Extensive experiments on nine graph-structured datasets have demonstrated the superior performance improvement on both convergence speed and predictive accuracy.