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.

How Important Are Work-Family Support Policies? A Meta-Analytic Investigation of Their Effects on Employee Outcomes

Abstract: This meta-analysis examines relationships between work-family support policies, which are policies that provide support for dependent care responsibilities, and employee outcomes by developing a conceptual model detailing the psychological mechanisms through which policy availability and use relate to work attitudes. Bivariate results indicated that availability and use of work-family support policies had modest positive relationships with job satisfaction, affective commitment, and intentions to stay. Further, tests of differences in effect sizes showed that policy availability was more strongly related to job satisfaction, affective commitment, and intentions to stay than was policy use. Subsequent meta-analytic structural equation modeling results indicated that policy availability and use had modest effects on work attitudes, which were partially mediated by family-supportive organization perceptions and work-to-family conflict, respectively. Additionally, number of policies and sample characteristics (percent women, percent married-cohabiting, percent with dependents) moderated the effects of policy availability and use on outcomes. Implications of these findings and directions for future research on work-family supportpolicies are discussed.

Trust in Forecast Information Sharing

Abstract: This paper investigates the capacity investment decision of a supplier who solicits private forecast information from a manufacturer. To ensure abundant supply, the manufacturer has an incentive to inflate her forecast in a costless, non-binding, and non-verifiable type of communication known as "cheap talk.'' According to standard game theory, parties do not cooperate and the only equilibrium is uninformative -- the manufacturer's report is independent of her forecast and the supplier does not use the report to determine capacity. However, we observe in controlled laboratory experiments that parties cooperate even in the absence of reputation-building mechanisms and complex contracts. We argue that the underlying reason for cooperation is trust. The extant literature on forecast sharing and supply chain coordination implicitly assumes that supply chain members either absolutely trust each other and cooperate when sharing forecast information, or do not trust each other at all. Contrary to this all-or-nothing view, we determine that a continuum exists between these two extremes. In addition, we determine (i) when trust is important in forecast information sharing, (ii) how trust is affected by changes in the supply chain environment, and (iii) how trust affects related operational decisions. To explain and better understand the observed behavioral regularities, we also develop an analytical model of trust to incorporate both pecuniary and non-pecuniary incentives in the game-theoretic analysis of cheap-talk forecast communication. The model identifies and quantifies how trust and trustworthiness induce effective cheap-talk forecast sharing under the wholesale price contract. We also determine the impact of repeated interactions and information feedback on trust and cooperation in forecast sharing. We conclude with a discussion on the implications of our results for developing effective forecast management policies.

A review of materials, fabrication methods, and strategies used to enhance bone regeneration in engineered bone tissues

Abstract: Over the last decade, bone engineered tissues have been developed as alternatives to autografts and allografts to repair and reconstruct bone defects. This article provides a review of the current technologies in bone tissue engineering. Factors used for fabrication of three-dimensional bone scaffolds such as materials, cells, and biomolecular signals, as well as required properties for ideal bone scaffolds, are reviewed. In addition, current fabrication techniques including rapid prototyping are elaborated upon. Finally, this review article further discusses some effective strategies to enhance cell ingrowth in bone engineered tissues; for example, nanotopography, biomimetic materials, embedded growth factors, mineralization, and bioreactors. In doing so, it suggests that there is a possibility to develop bone substitutes that can repair bone defects and promote new bone formation for orthopedic applications.

The influence of social ties and self-efficacy in forming entrepreneurial intentions and motivating nascent behavior

Abstract: Theoretical models of entrepreneurship suggest that an individual's intention to start an enterprise is a strong predictor of eventual entrepreneurial action. Less understood are factors that influence the likelihood of entrepreneurial intentions and nascent behavior. In this study, we develop and test several hypotheses about how social network ties and self-efficacy affect entrepreneurial intentions and nascent behavior. We found that a personal network of supportive strong ties coupled with high entrepreneurial self-efficacy increases the likelihood of entrepreneurial intentions and nascent behavior. A personal network of weak ties with practical business knowledge and experience also increases the likelihood of entrepreneurial nascent behavior but not entrepreneurial intentions. In contrast, a personal network of strong ties with practical business knowledge and experience has little effect on either intentions or nascent behavior and may, in fact, suppress both. The contribution of this study to nascent entrepreneurship research and implications for future research are discussed.

Forecasting power output of photovoltaic system based on weather classification and support vector machine

Abstract: Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for the system reliability and promoting large scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machine. In the process, the weather conditions are firstly divided into four types which are clear sky, cloudy day, foggy and rainy day. One-day-ahead PV power output forecasting model for single station is derived based on the weather forecasting data and historically actual power output data as well as the principle of Support Vector Machine (SVM). After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected photovoltaic systems is effective and promising.