Since business transformation is primarily about fundamental changes in organizational culture, the purpose of this article is to argue that Douglas's theory provides an effective framework for analyzing and understanding the cultural changes which are necessary for successful organizational transformation.
However, we have only rather limited methods for analyzing and understanding these collections.
Latent class analysis (LCA) is a statistical method for analyzing and understanding multivariate categorical data.
A general framework for analyzing and understanding the dynamics of reactive separation processes is developed.
For accurate diagnosis of related pathology, it has become necessary to develop new methods for analyzing and understanding this data.
My research develops computational frameworks for analyzing and understanding large-scale and complex datasets from the Web, social networks, biology, and other scientific domains.
Therefore, the paper discusses the development and usage of a new technique, such as Logic Programming Technique (LPT), for analyzing and understanding accidents.
With the increasing emphasis on supply chain vulnerabilities, effective mathematical tools for analyzing and understanding appropriate supply chain risk management are now attracting much attention.
These sequence information have been used extensively for analyzing and understanding genome structures and complexities, comparative and functional genomics and to mine useful genes and molecular markers.
My main research interest is in finding the most pertinent mathematical principles for analyzing and understanding high-dimensional sensorial data such as images, so that machines and human can make more intelligent decisions.
Techniques for fast, noninvasive measurement of neuronal excitability within a broad area will be of major importance for analyzing and understanding neuronal networks and animal behavior in neuroscience field.
