University of Virginia
Education•Charlottesville, Virginia, United States•
About: University of Virginia is a(n) education organization based out in Charlottesville, Virginia, United States. It is known for research contribution in the topic(s): Population & Poison control. The organization has 52543 authors who have published 113268 publication(s) receiving 5220506 citation(s). The organization is also known as: U of V & UVa.
Topics: Population, Poison control, Galaxy, Health care, Star formation
Papers published on a yearly basis
TL;DR: The Unified Theory of Acceptance and Use of Technology (UTAUT) as mentioned in this paper is a unified model that integrates elements across the eight models, and empirically validate the unified model.
Abstract: Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R2 of 70 percent). UTAUT thus provides a useful tool for managers needing to assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.
01 Mar 2010
01 Jan 1984
TL;DR: The Stakeholder Approach: 1. Managing in turbulent times 2. The stakeholder concept and strategic management 3. Strategic Management Processes: 4. Setting strategic direction 5. Formulating strategies for stakeholders 6. Implementing and monitoring stakeholder strategies 7. Conflict at the board level 8. The functional disciplines of management 9. The role of the executive as mentioned in this paper.
Abstract: Part I. The Stakeholder Approach: 1. Managing in turbulent times 2. The stakeholder concept and strategic management 3. Stakeholder management: framework and philosophy Part II. Strategic Management Processes: 4. Setting strategic direction 5. Formulating strategies for stakeholders 6. Implementing and monitoring stakeholder strategies Part III. Implications for Theory and Practice: 7. Conflict at the board level 8. The functional disciplines of management 9. The role of the executive.
15 Mar 2010-Bioinformatics
TL;DR: A new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format, which allows the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks.
Abstract: Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing webbased methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools
TL;DR: Three computer programs for comparisons of protein and DNA sequences can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity.
Abstract: We have developed three computer programs for comparisons of protein and DNA sequences. They can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity. The FASTA program is a more sensitive derivative of the FASTP program, which can be used to search protein or DNA sequence data bases and can compare a protein sequence to a DNA sequence data base by translating the DNA data base as it is searched. FASTA includes an additional step in the calculation of the initial pairwise similarity score that allows multiple regions of similarity to be joined to increase the score of related sequences. The RDF2 program can be used to evaluate the significance of similarity scores using a shuffling method that preserves local sequence composition. The LFASTA program can display all the regions of local similarity between two sequences with scores greater than a threshold, using the same scoring parameters and a similar alignment algorithm; these local similarities can be displayed as a "graphic matrix" plot or as individual alignments. In addition, these programs have been generalized to allow comparison of DNA or protein sequences based on a variety of alternative scoring matrices.
Showing all 52543 results
|Gordon B. Mills||187||1273||186451|
|Gonçalo R. Abecasis||179||595||230323|
|John R. Yates||177||1036||129029|
|John A. Rogers||177||1341||127390|
|Carl W. Cotman||165||809||105323|
|Ralph A. DeFronzo||160||759||132993|
|Dan R. Littman||157||426||107164|
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