ORCID Profile
0000-0001-9715-0010
Current Organisations
University of Melbourne
,
Rajamangala University of Technology Isan
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Publisher: Elsevier BV
Date: 02-2019
Publisher: Springer Science and Business Media LLC
Date: 26-11-2021
Publisher: Frontiers Media SA
Date: 02-03-2018
Publisher: Elsevier BV
Date: 10-2013
Publisher: Elsevier BV
Date: 11-2015
Publisher: AIP Publishing
Date: 02-2018
DOI: 10.1063/1.5019748
Publisher: MDPI AG
Date: 23-06-2021
DOI: 10.3390/SU13137046
Abstract: The aviation industry has grown rapidly worldwide and is struggling against intense competition. Especially in Thailand, the compound annual growth rate of passengers traveling by air has increased continuously over the past decade. Unfortunately, during the past two years, the ongoing COVID-19 pandemic has caused severe economic crises for nearly all businesses and industries, including the aviation industry and especially for passenger airlines whose number of customers has decreased astoundingly due to travel restriction. To maintain business stability, therefore, airlines must build customer loyalty to survive in times of crisis. This study thus examines critical factors’ impact on airline loyalty by using a Bayesian network (BN) derived from a structural equation modeling (SEM). The study integrates the SEM and BN to refine causal relationships between critical factors, identified as critical pathways. Findings reveal that customer satisfaction and customer trust, followed by perceived value, dramatically influence customer loyalty and so are considered priorities for building airlines’ customer loyalty. This study also recommends practical strategies and policies to improve customer loyalty amid the competitive airline business during and after the COVID-19 era.
Publisher: Cambridge University Press (CUP)
Date: 12-2021
DOI: 10.1086/715078
Abstract: To make sense of large data sets, we often look for patterns in how data points are “shaped” in the space of possible measurement outcomes. The emerging field of topological data analysis (TDA) offers a toolkit for formalizing the process of identifying such shapes. This article aims to discover why and how the resulting analysis should be understood as reflecting significant features of the systems that generated the data. I argue that a particular feature of TDA—its functoriality—is what enables TDA to translate visual intuitions about structure in data into precise, computationally tractable descriptions of real-world systems.
Publisher: AIP Publishing
Date: 10-2016
DOI: 10.1063/1.4965445
Abstract: A classic result in the foundations of Yang-Mills theory, due to Barrett [Int. J. Theor. Phys. 30, 1171–1215 (1991)], establishes that given a “generalized” holonomy map from the space of piece-wise smooth, closed curves based at some point of a manifold to a Lie group, there exists a principal bundle with that group as structure group and a principal connection on that bundle such that the holonomy map corresponds to the holonomies of that connection. Barrett also provided one sense in which this “recovery theorem” yields a unique bundle, up to isomorphism. Here we show that something stronger is true: with an appropriate definition of isomorphism between generalized holonomy maps, there is an equivalence of categories between the category whose objects are generalized holonomy maps on a smooth, connected manifold and whose arrows are holonomy isomorphisms, and the category whose objects are principal connections on principal bundles over a smooth, connected manifold. This result clarifies, and somewhat improves upon, the sense of “unique recovery” in Barrett’s theorems it also makes precise a sense in which there is no loss of structure involved in moving from a principal bundle formulation of Yang-Mills theory to a holonomy, or “loop,” formulation.
No related grants have been discovered for Sarita Rosenstock.