Samyukta Bhupatiraju - Academia.edu (original) (raw)
Uploads
Papers by Samyukta Bhupatiraju
Research Policy, 2012
This paper applies network analysis to a citation database that combines the key references in th... more This paper applies network analysis to a citation database that combines the key references in the fields of Entrepreneurship (ENT), Innovation Studies (INN) and Science and Technology Studies (STS). We find that citations between the three fields are relatively scarce, as compared to citations within the fields. As a result of this tendency, a cluster analysis of the publications in the database yields a partition that is largely the same as the a priori division into the three fields. We take this as evidence that the three fields, although they share research topics and themes, have developed largely on their own and in relative isolation from one another. We also apply a so-called 'main path' analysis aimed at outlining the main research trajectories in the field. Here we find important differences between the fields. In STS, we find a cumulative trajectory that develops in a more or less linear fashion over time. In INN, we find a major shift of attention in the main trajectory, from macroeconomic issues to business-oriented research. ENT develops relatively late, and shows a trajectory that is still in its infancy.
We propose a method for spatial principal components analysis that has two important advantages o... more We propose a method for spatial principal components analysis that has two important advantages over the method that Wartenberg (1985) proposed. The first advantage is that,
contrary to Wartenberg’s method, our method has a clear and exact interpretation: it produces a summary measure (component) that itself has maximum spatial correlation. Second, an easy and intuitive link can be made to canonical correlation analysis. Our spatial canonical correlation analysis produces summary measures of two datasets (e.g., each measuring a different phenomenon), and these summary measures maximize the spatial correlation between themselves. This provides an alternative weighting scheme as compared to spatial principal components analysis. We provide example applications of the methods and show that our variant of spatial canonical correlation analysis may produce rather different results than spatial principal components analysis using Wartenberg’s method. We also illustrate how
spatial canonical correlation analysis may produce different results than spatial principal components analysis.
Research Policy, 2012
This paper applies network analysis to a citation database that combines the key references in th... more This paper applies network analysis to a citation database that combines the key references in the fields of Entrepreneurship (ENT), Innovation Studies (INN) and Science and Technology Studies (STS). We find that citations between the three fields are relatively scarce, as compared to citations within the fields. As a result of this tendency, a cluster analysis of the publications in the database yields a partition that is largely the same as the a priori division into the three fields. We take this as evidence that the three fields, although they share research topics and themes, have developed largely on their own and in relative isolation from one another. We also apply a so-called 'main path' analysis aimed at outlining the main research trajectories in the field. Here we find important differences between the fields. In STS, we find a cumulative trajectory that develops in a more or less linear fashion over time. In INN, we find a major shift of attention in the main trajectory, from macroeconomic issues to business-oriented research. ENT develops relatively late, and shows a trajectory that is still in its infancy.
We propose a method for spatial principal components analysis that has two important advantages o... more We propose a method for spatial principal components analysis that has two important advantages over the method that Wartenberg (1985) proposed. The first advantage is that,
contrary to Wartenberg’s method, our method has a clear and exact interpretation: it produces a summary measure (component) that itself has maximum spatial correlation. Second, an easy and intuitive link can be made to canonical correlation analysis. Our spatial canonical correlation analysis produces summary measures of two datasets (e.g., each measuring a different phenomenon), and these summary measures maximize the spatial correlation between themselves. This provides an alternative weighting scheme as compared to spatial principal components analysis. We provide example applications of the methods and show that our variant of spatial canonical correlation analysis may produce rather different results than spatial principal components analysis using Wartenberg’s method. We also illustrate how
spatial canonical correlation analysis may produce different results than spatial principal components analysis.