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Charlotte H. Mason
Researcher at University of Georgia
Publications - 31
Citations - 6570
Charlotte H. Mason is an academic researcher from University of Georgia. The author has contributed to research in topics: Digital marketing & Customer relationship management. The author has an hindex of 22, co-authored 31 publications receiving 6108 citations. Previous affiliations of Charlotte H. Mason include University of North Carolina at Chapel Hill & Terry College of Business.
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Market orientation, marketing capabilities, and firm performance
TL;DR: It is found that market orientation has a direct effect on firms' return on assets (ROA), and that marketing capabilities directly impact both ROA and perceived firm performance.
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Collinearity, power, and interpretation of multiple regression analysis.
TL;DR: Multiple regression analysis is one of the most widely used statistical procedures for both scholarly and applied marketing research as discussed by the authors. Yet, correlated predictor variables, and potential collinearity of correlated predictors have not yet been explored.
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An Empirical Study of Innate Consumer Innovativeness, Personal Characteristics, and New-Product Adoption Behavior
TL;DR: In this article, the authors explored the relationship between consumer innovativeness, personal characteristics, and new-product adoption behavior and found that the personal characteristics of age and income are stronger predictors of new product ownership in the consumer electronics category.
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Technical Note---Nonlinear Least Squares Estimation of New Product Diffusion Models
V. Srinivasan,Charlotte H. Mason +1 more
TL;DR: Schmittlein and Mahajan as discussed by the authors proposed a nonlinear least squares (NLS) approach to estimate the standard error of the diffusion model, and the fit and the predictive validity were roughly comparable for the two approaches.
Posted Content
Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models
TL;DR: In this article, the authors provide a descriptive analysis of how methodological factors contribute to the accuracy of customer churn predictive models, based on a tournament in which both academics and practitioners downloaded data from a publicly available Web site, estimated a model, and made predictions on two validation databases.