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Institution

University of Dallas

EducationIrving, Texas, United States
About: University of Dallas is a education organization based out in Irving, Texas, United States. It is known for research contribution in the topics: Neutron scattering & Neutrino oscillation. The organization has 422 authors who have published 715 publications receiving 12300 citations.


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Journal ArticleDOI
TL;DR: In this paper, the relationship between HRM and performance was explored in 366 UK companies using objective and subjective performance measures and cross-sectional and longitudinal data, and it was shown that greater use of HRM is associated with lower labour turnover and higher profit per employee but not higher productivity.
Abstract: The relationship between HRM and performance was explored in 366 UK companies using objective and subjective performance measures and cross-sectional and longitudinal data. Using objective measures of performance, greater use of HRM is associated with lower labour turnover and higher profit per employee but not higher productivity. After controlling for previous years’ performance, the association ceases to be significant. Using subjective performance estimates, there is a strong association between HRM and both productivity and financial performance. The study therefore confirms the association between HRM and performance but fails to show that HRM causes higher performance.

679 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigate how the social cues inherent in avatars influence consumers' affect and shopping value, and find that social cues induce perceptions of Web site socialness, leading to increased pleasure and arousal, both of which positively influence flow, hedonic and utilitarian value.
Abstract: Avatars are lifelike characters created by technology. Research suggests that avatars can increase the persuasiveness of online sales channels. The authors investigate how the social cues inherent in avatars influence consumers' affect and shopping value. In Study 1, social cues induce perceptions of Web site socialness, leading to increased pleasure and arousal, both of which positively influence flow, hedonic and utilitarian value, and patronage intentions. Study 2 finds that social cue–induced arousal leads to increased pleasure only for consumers who are involved with the product category. Moreover, the influence of arousal on hedonic value is stronger for women, flow does not lead to pleasure for older consumers, and utilitarian value is less important for this group than for their younger counterparts. The findings suggest that there is a competitive advantage for online retailers that use social cues that provide consumers with enhanced perceptions of human connection and the formation of ...

595 citations

Journal ArticleDOI
TL;DR: A comprehensive survey of the major applications of deep learning covering variety of areas is presented, study of the techniques and architectures used and further the contribution of that respective application in the real world are presented.
Abstract: Nowadays, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost efficient machine learning approach. Deep learning is not a restricted learning approach, but it abides various procedures and topographies which can be applied to an immense speculum of complicated problems. The technique learns the illustrative and differential features in a very stratified way. Deep learning methods have made a significant breakthrough with appreciable performance in a wide variety of applications with useful security tools. It is considered to be the best choice for discovering complex architecture in high-dimensional data by employing back propagation algorithm. As deep learning has made significant advancements and tremendous performance in numerous applications, the widely used domains of deep learning are business, science and government which further includes adaptive testing, biological image classification, computer vision, cancer detection, natural language processing, object detection, face recognition, handwriting recognition, speech recognition, stock market analysis, smart city and many more. This paper focuses on the concepts of deep learning, its basic and advanced architectures, techniques, motivational aspects, characteristics and the limitations. The paper also presents the major differences between the deep learning, classical machine learning and conventional learning approaches and the major challenges ahead. The main intention of this paper is to explore and present chronologically, a comprehensive survey of the major applications of deep learning covering variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.

499 citations

Journal ArticleDOI
Cathy Bennett1, Nimish Vakil2, Jacques J. Bergman3, Rebecca Harrison4, Robert D. Odze5, Michael Vieth, Scott Sanders6, Oliver Pech, Gaius Longcroft-Wheaton7, Yvonne Romero8, John M. Inadomi9, Jan Tack10, Douglas A. Corley11, Hendrik Manner, Susi Green7, David Al Dulaimi, Haythem Ali12, Bill Allum13, Mark R Anderson, Howard Curtis14, Gary W. Falk15, M. Brian Fennerty16, Grant Fullarton17, Kausilia K. Krishnadath3, Stephen J. Meltzer18, David Armstrong19, Robert A. Ganz, Gianpaolo Cengia20, James J. Going17, John R. Goldblum21, Charles Gordon22, Heike I. Grabsch23, Chris Haigh, Michio Hongo24, David Johnston25, Ricky Forbes-Young26, Elaine Kay27, Philip Kaye28, Toni Lerut10, Laurence Lovat29, Lars Lundell30, Philip Mairs31, Tadakuza Shimoda32, Stuart J. Spechler33, Stephen J. Sontag34, Peter Malfertheiner35, Iain A. Murray, Manoj Nanji14, David N. Poller7, Krish Ragunath28, Jaroslaw Regula36, Renzo Cestari20, Neil A. Shepherd37, Rajvinder Singh38, Hubert J. Stein, Nicholas J. Talley39, Jean Paul Galmiche40, Tony C.K. Tham41, Peter Watson1, Lisa Yerian21, Massimo Rugge42, Thomas W. Rice21, John Hart43, Stuart Gittens, David Hewin37, Juergen Hochberger, Peter J. Kahrilas44, Sean L. Preston45, Richard E. Sampliner46, Prateek Sharma47, Robert C. Stuart, Kenneth K. Wang8, Irving Waxman43, Chris Abley4, Duncan Loft, Ian D. Penman26, Nicholas J. Shaheen48, Amitabh Chak49, Gareth Davies50, L. J. Dunn51, Yngve Falck-Ytter, John deCaestecker4, Pradeep Bhandari7, Christian Ell, S. Michael Griffin51, Stephen Attwood52, Hugh Barr37, John J.B. Allen53, Mark K. Ferguson43, Paul Moayyedi19, Janusz Jankowski14, Janusz Jankowski4, Janusz Jankowski54 
Queen's University Belfast1, University of Wisconsin-Madison2, University of Amsterdam3, University Hospitals of Leicester NHS Trust4, Harvard University5, University of Warwick6, Queen Alexandra Hospital7, Mayo Clinic8, University of Washington9, Katholieke Universiteit Leuven10, Kaiser Permanente11, Maidstone and Tunbridge Wells NHS Trust12, The Royal Marsden NHS Foundation Trust13, Queen Mary University of London14, University of Pennsylvania15, Oregon Health & Science University16, Glasgow Royal Infirmary17, Johns Hopkins University18, McMaster University19, University of Brescia20, Cleveland Clinic21, Christchurch Hospital22, University of Leeds23, Tohoku University24, Ninewells Hospital25, University of Edinburgh26, Trinity College, Dublin27, Nottingham University Hospitals NHS Trust28, University College London29, Karolinska Institutet30, Valley Hospital31, National Cancer Research Institute32, University of Dallas33, Veterans Health Administration34, Otto-von-Guericke University Magdeburg35, Curie Institute36, Gloucestershire Hospitals NHS Foundation Trust37, University of Adelaide38, University of Newcastle39, University of Nantes40, Ulster Hospital41, University of Padua42, University of Chicago43, Northwestern University44, Barts Health NHS Trust45, University of Arizona46, University of Kansas47, University of North Carolina at Chapel Hill48, Case Western Reserve University49, Harrogate and District NHS Foundation Trust50, Royal Victoria Infirmary51, Durham University52, University of Minnesota53, University of Oxford54
TL;DR: An international, multidisciplinary, systematic, evidence-based review of different management strategies for patients with Barrett's esophagus and dysplasia or early-stage EA and developed a data-sifting platform and used the Delphi process to create evidence- based consensus statements.

359 citations

Journal ArticleDOI
TL;DR: In this paper, a fuzzy set theoretic approach is used to demonstrate how different combinations of monitoring and incentive-based corporate governance mechanisms lead to the same level of investor valuations of firms.
Abstract: We build on sociology-grounded research on financial market behavior and suggest a “nested” legitimacy framework to explore U.S. investor perceptions of foreign IPO value. We draw on a fuzzy-set theoretic approach to demonstrate how different combinations of monitoring and incentive-based corporate governance mechanisms lead to the same level of investor valuations of firms. We also argue that institutional factors related to the minority shareholder protection strength in the foreign IPO’s home country represent a boundary condition that affects the number of governance mechanisms required to achieve U.S. investors’ high value perceptions. Our findings, drawn from a unique, hand-collected dataset of foreign IPOs in the U.S, contribute to the sociological perspective on comparative corporate governance and the inter-dependencies between organizations and institutions.

338 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20232
202223
202134
202040
201938
201835